diff --git a/Airlinepassengerreferral_predicton__1_.ipynb b/Airlinepassengerreferral_predicton__1_.ipynb
index 80a768be23e37b08c76b3b3293d521c1dcc13843..c1fc272a6624bc42bd2f77e2fbfc6401d748df59 100644
--- a/Airlinepassengerreferral_predicton__1_.ipynb
+++ b/Airlinepassengerreferral_predicton__1_.ipynb
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@@ -2472,7 +2472,7 @@
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+   "execution_count": 33,
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@@ -2562,7 +2562,7 @@
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@@ -2607,7 +2607,7 @@
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+   "execution_count": 36,
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@@ -2625,7 +2625,7 @@
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@@ -2639,7 +2639,7 @@
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@@ -33330,9 +33330,9 @@
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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('dd21c4ab-ab1a-449d-9a7a-241a58eeea01');\n",
+       "var gd = document.getElementById('6594c445-6993-48ad-89cf-90cd28cf96db');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -33388,7 +33388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -33419,7 +33419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "id": "rE-4TENmin-P"
    },
@@ -33431,7 +33431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "id": "N5ANZppMfsKq"
    },
@@ -33451,7 +33451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -33494,7 +33494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "id": "94xRV2gKjXPo"
    },
@@ -33505,7 +33505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -33523,7 +33523,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -33534,7 +33534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "id": "8T6q-4DM79HM"
    },
@@ -33554,7 +33554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -33731,7 +33731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "id": "bGT9lH07Jslz"
    },
@@ -33742,6 +33742,16 @@
     "# Dropping the 'airline' column as it is no more needed for further predictive analysis."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the DataFrame to a CSV file without the index\n",
+    "airline_df.to_csv('cleanedairline_pred_data.csv', index=False)\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -33753,7 +33763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "id": "wYLkkdmxJ6ZE"
    },
@@ -33768,16 +33778,6 @@
     "airline_df.drop(['traveller_type','cabin'],axis=1,inplace=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Save the DataFrame to a CSV file without the index\n",
-    "airline_df.to_csv('cleanedairline_data.csv', index=False)\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -33789,7 +33789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "id": "C2WHHqiRDD_r"
    },
@@ -33802,7 +33802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -33817,7 +33817,7 @@
        "53.200363257206284"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -33828,7 +33828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -33843,7 +33843,7 @@
        "46.799636742793716"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -33854,7 +33854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "id": "nJ9rTJjYHvJv"
    },
@@ -33869,7 +33869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -33895,7 +33895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -33921,7 +33921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -33967,7 +33967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "id": "kULpJOi8H8Sx"
    },
@@ -33980,7 +33980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "id": "cQbmgg_ZIHD3"
    },
@@ -33995,7 +33995,9 @@
     "        'PRECISION': [precision_score(Y_test, Y_pred, average='binary')],  # Adjust as necessary\n",
     "        'F1-SCORE': [f1_score(Y_test, Y_pred, average='binary')],  # Adjust as necessary\n",
     "        'ROC AUC SCORE': [roc_auc_score(Y_test, Y_pred)]  # Make sure Y_pred are probabilities if required\n",
-    "    })"
+    "    })\n",
+    "\n",
+    "    return pd.concat([df, new_row], ignore_index=True)"
    ]
   },
   {
@@ -34018,7 +34020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "id": "b235fa57",
     "outputId": "dbeba9ca-8145-4516-a3d8-2d66e8587c5f"
@@ -34033,7 +34035,7 @@
        "DecisionTreeClassifier()"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34047,7 +34049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "id": "ab8bfa30",
     "outputId": "f0b726bd-92fe-4f09-8acb-fc857cd35af7",
@@ -34059,7 +34061,7 @@
      "output_type": "stream",
      "text": [
       "Training Accuracy of Decision Tree Model is 0.9953329969727548\n",
-      "Testing Accuracy of Decision Tree Model is 0.9250042038002354\n"
+      "Testing Accuracy of Decision Tree Model is 0.9244997477719858\n"
      ]
     }
    ],
@@ -34070,7 +34072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "id": "33629d40"
    },
@@ -34083,7 +34085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "id": "b424a17b",
     "outputId": "2486b767-d83f-4e1e-a35e-5a6eb55a0922"
@@ -34113,7 +34115,7 @@
        "             scoring=['f1', 'recall', 'precision', 'accuracy'])"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34130,7 +34132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "id": "1f18bc90",
     "outputId": "1a49b7c0-2fda-451f-bd6c-4868fa41fd99"
@@ -34145,7 +34147,7 @@
        " 'min_samples_split': 5}"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34157,7 +34159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "id": "d0505bf0",
     "outputId": "ab3c3433-5864-4b51-ae80-ab40560d62ac"
@@ -34169,7 +34171,7 @@
        "0.9511436203174888"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34181,7 +34183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {
     "id": "d4af37e2",
     "outputId": "d571539f-3e83-44d1-8271-19d570f77915"
@@ -34199,7 +34201,7 @@
        "                       min_samples_split=5)"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34212,7 +34214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "metadata": {
     "id": "40e897f8",
     "outputId": "1b4cf01c-00d9-4417-d7a2-9f1610355dd8"
@@ -34241,7 +34243,7 @@
        "Text(0.5, 1.0, 'Confusion Matrix for Decision Tree  Model')"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -34280,7 +34282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "metadata": {
     "id": "eed30370",
     "outputId": "dd496ed7-40d8-43fe-89ac-37f78f6dd762"
@@ -34292,7 +34294,7 @@
        "0.9487136371279636"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34306,7 +34308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "id": "5371acc9",
     "outputId": "9b4391ef-d576-47ae-dbb3-116391332514"
@@ -34356,7 +34358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "id": "545ac715"
    },
@@ -34368,7 +34370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "id": "c9443b7b",
     "outputId": "09c20dd7-7e4d-4407-d1a9-9d23a80f418a"
@@ -34389,7 +34391,7 @@
        "       44, 46, 48])})"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34403,7 +34405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "metadata": {
     "id": "dc7a01e5",
     "outputId": "2cc84915-7e2a-4c8c-cb66-d7249a873006"
@@ -34418,7 +34420,7 @@
        "KNeighborsClassifier(n_neighbors=38)"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34430,7 +34432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "metadata": {
     "id": "487d5448"
    },
@@ -34441,7 +34443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "metadata": {
     "id": "716f1ac2",
     "outputId": "64c02906-a0ec-450c-e267-1a813668fedd"
@@ -34488,18 +34490,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "id": "1843fb15"
-   },
-   "outputs": [],
-   "source": [
-    "# Adding model performance in evaluation metric dataframe.\n",
-    "metrics_df=add_metrics_details(\"KNN - GridSearchCV\",Y_test,Y_pred,metrics_df)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -34511,16 +34501,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 72,
    "metadata": {
     "id": "e0f3952f",
     "outputId": "19a0bf12-4e8f-428c-e89a-5910214e57b4"
    },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.95      0.96      0.96      3136\n",
+      "           1       0.96      0.94      0.95      2811\n",
+      "\n",
+      "    accuracy                           0.95      5947\n",
+      "   macro avg       0.95      0.95      0.95      5947\n",
+      "weighted avg       0.95      0.95      0.95      5947\n",
+      "\n",
+      "Accuracy of the Model: 95.22448293257104%\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
+    "\n",
+    "# SVM model implementation\n",
+    "svm = SVC(probability=True)\n",
+    "svm.fit(X_train, Y_train)\n",
+    "\n",
+    "# Evaluation of the SVM model\n",
+    "Y_pred = svm.predict(X_test)\n",
+    "\n",
+    "print(classification_report(Y_test, Y_pred))\n",
+    "cm = confusion_matrix(Y_test, Y_pred)\n",
+    "\n",
+    "print(\"Accuracy of the Model: {}%\\n\".format((accuracy_score(Y_test, Y_pred))*100))\n",
+    "plt.figure(figsize=(10,5))\n",
+    "sns.heatmap(cm, annot=True, fmt=\".1f\", cmap=\"Greens\")\n",
+    "plt.title(\"Confusion Matrix for SVM Model\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adding model performance to evaluation metrics dataframe\n",
+    "metrics_df = add_metrics_details(\"SVM Model\", Y_test, Y_pred, metrics_df)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=SVC(probability=True), n_iter=5, n_jobs=-1,\n",
+       "                   param_distributions={&#x27;C&#x27;: [0.1, 1, 10], &#x27;gamma&#x27;: [0.1, 0.01],\n",
+       "                                        &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;poly&#x27;]},\n",
+       "                   random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=SVC(probability=True), n_iter=5, n_jobs=-1,\n",
+       "                   param_distributions={&#x27;C&#x27;: [0.1, 1, 10], &#x27;gamma&#x27;: [0.1, 0.01],\n",
+       "                                        &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;poly&#x27;]},\n",
+       "                   random_state=0)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
       "text/plain": [
-       "0.9540945014292921"
+       "RandomizedSearchCV(cv=3, estimator=SVC(probability=True), n_iter=5, n_jobs=-1,\n",
+       "                   param_distributions={'C': [0.1, 1, 10], 'gamma': [0.1, 0.01],\n",
+       "                                        'kernel': ['rbf', 'poly']},\n",
+       "                   random_state=0)"
       ]
      },
      "execution_count": 74,
@@ -34529,19 +34596,27 @@
     }
    ],
    "source": [
-    "from sklearn.svm import SVC\n",
-    "svc = SVC(kernel='linear')\n",
-    "svc.fit(X_train, Y_train)\n",
-    "svc.score(X_test, Y_test)"
+    "# Hyperparameter tuning for SVM\n",
+    "svm_params = {'C': [0.1, 1, 10], 'gamma': [0.1, 0.01], 'kernel': ['rbf', 'poly']}\n",
+    "svm_cv = RandomizedSearchCV(svm, svm_params, n_iter=5, cv=3, random_state=0, n_jobs=-1)\n",
+    "svm_cv.fit(X_train, Y_train)\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 75,
-   "metadata": {
-    "id": "919bf13d",
-    "outputId": "9e0b8e70-3ea0-4cd9-9c32-b8657ad993a1"
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Best estimator for SVM\n",
+    "svm_cv_best = svm_cv.best_estimator_\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -34550,19 +34625,19 @@
       "              precision    recall  f1-score   support\n",
       "\n",
       "           0       0.95      0.96      0.96      3136\n",
-      "           1       0.95      0.95      0.95      2811\n",
+      "           1       0.96      0.94      0.95      2811\n",
       "\n",
       "    accuracy                           0.95      5947\n",
       "   macro avg       0.95      0.95      0.95      5947\n",
       "weighted avg       0.95      0.95      0.95      5947\n",
       "\n",
-      "Accuracy of the Model: 95.4094501429292%\n",
+      "Accuracy of the Model: 95.3085589372793%\n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 2 Axes>"
       ]
@@ -34572,47 +34647,549 @@
     }
    ],
    "source": [
-    "# Evaluation of the model.\n",
-    "Y_pred = svc.predict(X_test)\n",
+    "# Evaluation of the best SVM model\n",
+    "Y_pred = svm_cv_best.predict(X_test)\n",
     "\n",
     "print(classification_report(Y_test, Y_pred))\n",
-    "cm = confusion_matrix( Y_test,Y_pred)\n",
+    "cm = confusion_matrix(Y_test, Y_pred)\n",
     "\n",
-    "print(\"Accuracy of the Model: {}%\\n\".format((accuracy_score( Y_test,Y_pred))*100))\n",
+    "print(\"Accuracy of the Model: {}%\\n\".format((accuracy_score(Y_test, Y_pred))*100))\n",
     "plt.figure(figsize=(10,5))\n",
-    "sns.heatmap(cm, annot=True, fmt = \".1f\",cmap=\"Reds\")\n",
+    "sns.heatmap(cm, annot=True, fmt=\".1f\", cmap=\"Blues\")\n",
     "plt.title(\"Confusion Matrix for SVM - GridSearchCV\")\n",
-    "plt.show()"
+    "plt.show()\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adding model performance to evaluation metrics dataframe\n",
+    "metrics_df = add_metrics_details(\"SVM - GridSearchCV\", Y_test, Y_pred, metrics_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
    "metadata": {
-    "id": "e37a30b4"
+    "id": "919bf13d",
+    "outputId": "9e0b8e70-3ea0-4cd9-9c32-b8657ad993a1"
    },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           MODEL NAME  ACCURACY    RECALL  PRECISION  F1-SCORE  ROC AUC SCORE\n",
+      "0           KNN Model  0.948714  0.940591   0.950395  0.945468       0.948293\n",
+      "1           SVM Model  0.952245  0.941302   0.956962  0.949067       0.951678\n",
+      "2  SVM - GridSearchCV  0.953086  0.941302   0.958696  0.949919       0.952475\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the final evaluation metrics for all models\n",
+    "print(metrics_df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "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>recommended</th>\n",
+       "      <th>overall</th>\n",
+       "      <th>seat_comfort</th>\n",
+       "      <th>cabin_service</th>\n",
+       "      <th>food_bev</th>\n",
+       "      <th>entertainment</th>\n",
+       "      <th>ground_service</th>\n",
+       "      <th>value_for_money</th>\n",
+       "      <th>Couple Leisure</th>\n",
+       "      <th>Family Leisure</th>\n",
+       "      <th>Solo Leisure</th>\n",
+       "      <th>Economy Class</th>\n",
+       "      <th>First Class</th>\n",
+       "      <th>Premium Economy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   recommended  overall  seat_comfort  cabin_service  food_bev  entertainment  \\\n",
+       "0            1      7.0           4.0            5.0       4.0            4.0   \n",
+       "1            0      2.0           4.0            1.0       1.0            1.0   \n",
+       "2            0      3.0           1.0            4.0       1.0            3.0   \n",
+       "3            1     10.0           4.0            5.0       5.0            5.0   \n",
+       "4            0      1.0           1.0            1.0       1.0            1.0   \n",
+       "\n",
+       "   ground_service  value_for_money  Couple Leisure  Family Leisure  \\\n",
+       "0             2.0              4.0           False           False   \n",
+       "1             1.0              1.0           False            True   \n",
+       "2             1.0              2.0           False           False   \n",
+       "3             5.0              5.0           False           False   \n",
+       "4             1.0              1.0           False           False   \n",
+       "\n",
+       "   Solo Leisure  Economy Class  First Class  Premium Economy  \n",
+       "0         False           True        False            False  \n",
+       "1         False           True        False            False  \n",
+       "2         False           True        False            False  \n",
+       "3          True           True        False            False  \n",
+       "4          True           True        False            False  "
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = pd.read_csv('cleanedairline_pred_data.csv')\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique values in 'recommended' column: [1 0]\n",
+      "Value counts of 'recommended' column:\n",
+      " recommended\n",
+      "0    15817\n",
+      "1    13914\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect the recommended column\n",
+    "print(\"Unique values in 'recommended' column:\", df2['recommended'].unique())\n",
+    "print(\"Value counts of 'recommended' column:\\n\", df2['recommended'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ILR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Facts have been successfully generated and saved to 'facts.pl'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the dataset\n",
+    "df2 = pd.read_csv('cleanedairline_pred_data.csv')\n",
+    "df2.head()\n",
+    "\n",
+    "\n",
+    "# Fill numerical NaNs with the mean\n",
+    "numerical_features = ['overall', 'seat_comfort', 'cabin_service', 'food_bev', 'entertainment', 'ground_service', 'value_for_money']\n",
+    "df2[numerical_features] = df2[numerical_features].fillna(df2[numerical_features].mean())\n",
+    "\n",
+    "# Convert `recommended` to binary\n",
+    "df2['recommended'] = df2['recommended'].apply(lambda x: 1 if x == 'yes' else 0)\n",
+    "\n",
+    "# Define mappings for traveller type and cabin based on the one-hot encoded columns\n",
+    "traveller_type_columns = ['Couple Leisure', 'Family Leisure', 'Solo Leisure']\n",
+    "cabin_columns = ['Economy Class', 'First Class', 'Premium Economy']\n",
+    "\n",
+    "def get_traveller_type(row):\n",
+    "    for col in traveller_type_columns:\n",
+    "        if row[col]:\n",
+    "            return col.replace(' ', '_')\n",
+    "    return 'Unknown'\n",
+    "\n",
+    "def get_cabin_type(row):\n",
+    "    for col in cabin_columns:\n",
+    "        if row[col]:\n",
+    "            return col.replace(' ', '_')\n",
+    "    return 'Unknown'\n",
+    "\n",
+    "# Save facts to a Prolog file\n",
+    "with open('facts.pl', 'w') as f:\n",
+    "    for _, row in df2.iterrows():\n",
+    "        traveller_type = get_traveller_type(row)\n",
+    "        cabin = get_cabin_type(row)\n",
+    "        fact = f\"recommended({traveller_type}, {cabin}, {int(row['seat_comfort'])}, {int(row['cabin_service'])}, {int(row['food_bev'])}, {int(row['entertainment'])}, {int(row['ground_service'])}, {int(row['value_for_money'])}).\\n\"\n",
+    "        f.write(fact)\n",
+    "print(\"Facts have been successfully generated and saved to 'facts.pl'.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['recommended', 'overall', 'seat_comfort', 'cabin_service', 'food_bev',\n",
+      "       'entertainment', 'ground_service', 'value_for_money', 'Couple Leisure',\n",
+      "       'Family Leisure', 'Solo Leisure', 'Economy Class', 'First Class',\n",
+      "       'Premium Economy'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df2.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Positive and negative examples have been successfully generated and saved to 'positiveexamples.f' and 'negativeexamples.n'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate positive and negative examples\n",
+    "positive_examples = []\n",
+    "negative_examples = []\n",
+    "for _, row in df2.iterrows():\n",
+    "    traveller_type = get_traveller_type(row)\n",
+    "    cabin = get_cabin_type(row)\n",
+    "    fact = f\"recommended({traveller_type}, {cabin}, {int(row['seat_comfort'])}, {int(row['cabin_service'])}, {int(row['food_bev'])}, {int(row['entertainment'])}, {int(row['ground_service'])}, {int(row['value_for_money'])}).\"\n",
+    "    if row['recommended'] == 1:\n",
+    "        positive_examples.append(fact)\n",
+    "    else:\n",
+    "        negative_examples.append(fact)\n",
+    "\n",
+    "# Save positive examples to file\n",
+    "with open('positiveexamples.f', 'w') as f:\n",
+    "    for example in positive_examples:\n",
+    "        f.write(example + '\\n')\n",
+    "\n",
+    "# Save negative examples to file\n",
+    "with open('negativeexamples.n', 'w') as f:\n",
+    "    for example in negative_examples:\n",
+    "        f.write(example + '\\n')\n",
+    "\n",
+    "print(\"Positive and negative examples have been successfully generated and saved to 'positiveexamples.f' and 'negativeexamples.n'.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of positive examples: 13914\n",
+      "Number of negative examples: 15817\n",
+      "Positive and negative examples have been successfully generated and saved to 'positiveexamples.f' and 'negativeexamples.n'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "traveller_type_columns = ['Couple Leisure', 'Family Leisure', 'Solo Leisure']\n",
+    "cabin_columns = ['Economy Class', 'First Class', 'Premium Economy']\n",
+    "\n",
+    "def get_traveller_type(row):\n",
+    "    for col in traveller_type_columns:\n",
+    "        if row[col]:\n",
+    "            return col.replace(' ', '_').lower()\n",
+    "    return 'unknown'\n",
+    "\n",
+    "def get_cabin_type(row):\n",
+    "    for col in cabin_columns:\n",
+    "        if row[col]:\n",
+    "            return col.replace(' ', '_').lower()\n",
+    "    return 'unknown'\n",
+    "\n",
+    "\n",
+    "positive_examples = []\n",
+    "negative_examples = []\n",
+    "\n",
+    "for _, row in df2.iterrows():\n",
+    "    traveller_type = get_traveller_type(row)\n",
+    "    cabin = get_cabin_type(row)\n",
+    "    fact = f\"recommended({traveller_type}, {cabin}, {int(row['seat_comfort'])}, {int(row['cabin_service'])}, {int(row['food_bev'])}, {int(row['entertainment'])}, {int(row['ground_service'])}, {int(row['value_for_money'])}).\"\n",
+    "    if row['recommended'] == 1:\n",
+    "        positive_examples.append(fact)\n",
+    "    else:\n",
+    "        negative_examples.append(fact)\n",
+    "\n",
+    "print(\"Number of positive examples:\", len(positive_examples))\n",
+    "print(\"Number of negative examples:\", len(negative_examples))\n",
+    "\n",
+    "# Save positive examples to file\n",
+    "with open('positiveexamples.f', 'w') as f:\n",
+    "    for example in positive_examples:\n",
+    "        f.write(example + '\\n')\n",
+    "\n",
+    "# Save negative examples to file\n",
+    "with open('negativeexamples.n', 'w') as f:\n",
+    "    for example in negative_examples:\n",
+    "        f.write(example + '\\n')\n",
+    "\n",
+    "print(\"Positive and negative examples have been successfully generated and saved to 'positiveexamples.f' and 'negativeexamples.n'.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Q learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Adding model performance in evaluation metric dataframe.\n",
-    "metrics_df=add_metrics_details(\"Support Vector Machine\",Y_test,Y_pred,metrics_df)"
+    "# Load the dataset\n",
+    "file_path = 'cleanedairline_pred_data.csv'\n",
+    "dataset = pd.read_csv(file_path)\n",
+    "\n",
+    "# Preprocess the data\n",
+    "scaler = MinMaxScaler()\n",
+    "numerical_features = ['overall', 'seat_comfort', 'cabin_service', 'food_bev', 'entertainment', 'ground_service', 'value_for_money']\n",
+    "dataset[numerical_features] = scaler.fit_transform(dataset[numerical_features])\n",
+    "\n",
+    "# Convert boolean features to integers\n",
+    "boolean_features = ['Couple Leisure', 'Family Leisure', 'Solo Leisure', 'Economy Class', 'First Class', 'Premium Economy']\n",
+    "dataset[boolean_features] = dataset[boolean_features].astype(int)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define states and actions\n",
+    "states = dataset.drop(columns=['recommended']).values\n",
+    "actions = np.unique(dataset['recommended'].values)\n",
+    "num_states = len(states)\n",
+    "num_actions = len(actions)\n",
+    "# Initialize Q-table\n",
+    "q_table = np.zeros((len(states), len(actions)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hyperparameters\n",
+    "alpha = 0.1\n",
+    "gamma = 0.95\n",
+    "epsilon = 0.1\n",
+    "num_episodes = 10\n",
+    "\n",
+    "\n",
+    "def choose_action(state_index):\n",
+    "    if np.random.uniform(0, 1) < epsilon:\n",
+    "        return np.random.choice(actions)  \n",
+    "    else:\n",
+    "        return np.argmax(q_table[state_index, :])  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "None\n"
+      "Training completed\n"
      ]
     }
    ],
    "source": [
-    "# Display the metrics DataFrame\n",
-    "print(metrics_df)"
+    "# Training the agent\n",
+    "for episode in range(num_episodes):\n",
+    "    \n",
+    "    state_index = np.random.randint(0, len(states))\n",
+    "    done = False\n",
+    "    \n",
+    "    while not done:\n",
+    "        action = choose_action(state_index)\n",
+    "      \n",
+    "        next_state_index = (state_index + 1) % len(states)\n",
+    "        reward = dataset.iloc[next_state_index]['recommended']\n",
+    "        \n",
+    "        # Q-learning update\n",
+    "        q_table[state_index, action] = q_table[state_index, action] + alpha * (\n",
+    "            reward + gamma * np.max(q_table[next_state_index, :]) - q_table[state_index, action])\n",
+    "        \n",
+    "        state_index = next_state_index\n",
+    "        done = state_index == 0\n",
+    "\n",
+    "print(\"Training completed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Policy accuracy: 0.53\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the learned policy\n",
+    "def evaluate_policy(q_table, states, actions):\n",
+    "    correct_predictions = 0\n",
+    "    total_predictions = len(states)\n",
+    "    \n",
+    "    for state_index in range(total_predictions):\n",
+    "        action = np.argmax(q_table[state_index, :])\n",
+    "        correct_predictions += int(action == dataset.iloc[state_index]['recommended'])\n",
+    "    \n",
+    "    accuracy = correct_predictions / total_predictions\n",
+    "    return accuracy\n",
+    "\n",
+    "accuracy = evaluate_policy(q_table, states, actions)\n",
+    "print(f\"Policy accuracy: {accuracy:.2f}\")"
    ]
   },
   {
@@ -34646,5 +35223,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }