diff --git "a/main.ipynb" "b/main.ipynb"
--- "a/main.ipynb"
+++ "b/main.ipynb"
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 184,
    "metadata": {},
    "outputs": [
     {
@@ -40,6 +40,9 @@
     "from sklearn.naive_bayes import MultinomialNB\n",
     "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, f1_score\n",
     "\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC \n",
+    "\n",
     "nltk.download(\"stopwords\")\n",
     "from nltk.corpus import stopwords\n",
     "\n",
@@ -56,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 185,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 186,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 187,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [
     {
@@ -119,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [
     {
@@ -146,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 190,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -186,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [
     {
@@ -214,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 196,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,13 +286,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 199,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {\n",
+       "<style>#sk-container-id-18 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -319,15 +322,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 {\n",
+       "#sk-container-id-18 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 pre {\n",
+       "#sk-container-id-18 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "#sk-container-id-18 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -339,7 +342,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-18 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -347,7 +350,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-container {\n",
+       "#sk-container-id-18 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -357,7 +360,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-18 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -373,14 +376,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "#sk-container-id-18 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
+       "#sk-container-id-18 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -388,28 +391,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "#sk-container-id-18 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-18 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-18 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-18 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
+       "#sk-container-id-18 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -427,14 +430,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
+       "#sk-container-id-18 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "#sk-container-id-18 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -444,7 +447,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-18 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -452,13 +455,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "#sk-container-id-18 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -467,12 +470,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-18 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-18 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -480,79 +483,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-18 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-18 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-18 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-18 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-18 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-18 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-18 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
+       "#sk-container-id-18 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
+       "#sk-container-id-18 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -562,18 +565,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "#sk-container-id-18 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "#sk-container-id-18 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-18 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -660,7 +663,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "#sk-container-id-18 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -675,173 +678,1273 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-18 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-18 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-18 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "MultinomialNB()"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Train the Naive Bayes Model\n",
-    "model = MultinomialNB()\n",
-    "model.fit(X_train, y_train)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Predictions\n",
-    "y_pred = model.predict(X_test)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h1>Model Evaluation</h1>\n",
-    "<h5>After training the model, we will use Evaluation metrics in order to judge if the model's predictions are correct.</h5>\n",
-    "<h5></h5>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.9398\n",
-      "F1 Score: 0.9375\n",
-      "\n",
-      "Classification Report:\n",
-      "               precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.91      0.97      0.94       150\n",
-      "           1       0.97      0.91      0.94       149\n",
-      "\n",
-      "    accuracy                           0.94       299\n",
-      "   macro avg       0.94      0.94      0.94       299\n",
-      "weighted avg       0.94      0.94      0.94       299\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "accuracy = accuracy_score(y_test, y_pred)\n",
-    "f1 = f1_score(y_test, y_pred)\n",
-    "report = classification_report(y_test, y_pred)\n",
-    "\n",
-    "print(f\"Accuracy: {accuracy:.4f}\")\n",
-    "print(f\"F1 Score: {f1:.4f}\")\n",
-    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "text_output = f\"Accuracy: {accuracy:.4f}\\nF1 Score: {f1:.4f}\\n\\nClassification Report:\\n{report}\"\n",
-    "\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.text(0.01, 0.99, text_output, fontsize=12, ha='left', va='top', family=\"monospace\")\n",
-    "# Hide axes\n",
-    "plt.axis(\"off\")\n",
-    "\n",
-    "plt.savefig(\"classification_report.png\", bbox_inches=\"tight\", dpi=300)\n",
-    "plt.show()"
+    "# FOR THE NAIVE BAYES\n",
+    "MNBmodel = MultinomialNB()\n",
+    "MNBmodel.fit(X_train, y_train)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 200,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
-    "plt.figure(figsize=(5, 4))\n",
-    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Ham\", \"Spam\"], yticklabels=[\"Ham\", \"Spam\"])\n",
-    "plt.xlabel(\"Predicted\")\n",
-    "plt.ylabel(\"Actual\")\n",
-    "plt.title(\"Confusion Matrix\")\n",
-    "plt.savefig(\"confusion_matrix.png\")\n",
-    "plt.show()\n",
-    "# Save the figure\n",
-    "plt.close() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model and vectorizer saved successfully!\n"
-     ]
-    }
-   ],
-   "source": [
-    "#SAVING THE MODEL AS A .pkl FILE\n",
-    "joblib.dump(model, \"spam_classifier.pkl\")\n",
-    "\n",
-    "#SAVING THE VECTORIZER AS A .pkl FILE\n",
-    "joblib.dump(vectorizer, \"tfidf_vectorizer.pkl\")\n",
-    "\n",
-    "print(\"Model and vectorizer saved successfully!\")"
+      "text/html": [
+       "<style>#sk-container-id-19 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-19 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-19 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-19 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-19 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-19 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-19 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-19 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# FOR THE LOGISTIC REGRESSION\n",
+    "LRmodel = LogisticRegression()\n",
+    "LRmodel.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-20 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-20 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-20 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-20 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-20 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-20 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-20 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-20 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVC(kernel='linear')"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# FOR THE SVM\n",
+    "SVMmodel = SVC(kernel=\"linear\")  # Linear kernel is effective for text classification\n",
+    "SVMmodel.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MULTINOMIAL NAIVE BAYES PREDICTION\n",
+    "MNBy_pred = MNBmodel.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# LOGISTIC REGRESSION PREDICTION\n",
+    "LRy_pred = LRmodel.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predictions\n",
+    "SVMy_pred = SVMmodel.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>Model Evaluation</h1>\n",
+    "<h5>After training the model, we will use Evaluation metrics in order to judge if the model's predictions are correct.</h5>\n",
+    "<h5></h5>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9398\n",
+      "F1 Score: 0.9375\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.91      0.97      0.94       150\n",
+      "           1       0.97      0.91      0.94       149\n",
+      "\n",
+      "    accuracy                           0.94       299\n",
+      "   macro avg       0.94      0.94      0.94       299\n",
+      "weighted avg       0.94      0.94      0.94       299\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# MULTINOMIAL NAIVE BAYES PERFORMANCE\n",
+    "accuracy = accuracy_score(y_test, MNBy_pred)\n",
+    "f1 = f1_score(y_test, MNBy_pred)\n",
+    "report = classification_report(y_test, MNBy_pred)\n",
+    "\n",
+    "print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "print(f\"F1 Score: {f1:.4f}\")\n",
+    "print(\"\\nClassification Report:\\n\", classification_report(y_test, MNBy_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "text_output = f\"Accuracy: {accuracy:.4f}\\nF1 Score: {f1:.4f}\\n\\nClassification Report:\\n{report}\"\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.text(0.01, 0.99, text_output, fontsize=12, ha='left', va='top', family=\"monospace\")\n",
+    "# Hide axes\n",
+    "plt.axis(\"off\")\n",
+    "\n",
+    "plt.savefig(\"MNBclassification_report.png\", bbox_inches=\"tight\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "conf_matrix = confusion_matrix(y_test, MNBy_pred)\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Ham\", \"Spam\"], yticklabels=[\"Ham\", \"Spam\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.title(\"Naive Bayes Confusion Matrix\")\n",
+    "\n",
+    "plt.savefig(\"MNBconfusion_matrix.png\")\n",
+    "plt.show()\n",
+    "plt.close() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model and vectorizer saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "#SAVING THE MODEL AS A .pkl FILE\n",
+    "joblib.dump(MNBmodel, \"MNBspam_classifier_model.pkl\")\n",
+    "\n",
+    "#SAVING THE VECTORIZER AS A .pkl FILE\n",
+    "joblib.dump(vectorizer, \"tfidf_vectorizer.pkl\")\n",
+    "\n",
+    "print(\"Model and vectorizer saved successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9398\n",
+      "F1 Score: 0.9375\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.86      0.98      0.92       150\n",
+      "           1       0.98      0.85      0.91       149\n",
+      "\n",
+      "    accuracy                           0.91       299\n",
+      "   macro avg       0.92      0.91      0.91       299\n",
+      "weighted avg       0.92      0.91      0.91       299\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# LOGISTIC REGRESSION PERFORMANCE\n",
+    "LRaccuracy = accuracy_score(y_test, LRy_pred)\n",
+    "LRf1 = f1_score(y_test, LRy_pred)\n",
+    "LRreport = classification_report(y_test, LRy_pred)\n",
+    "\n",
+    "print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "print(f\"F1 Score: {f1:.4f}\")\n",
+    "print(\"\\nClassification Report:\\n\", LRreport)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Save Logistic Regression report as image\n",
+    "LR_text_output = f\"Accuracy: {LRaccuracy:.4f}\\nF1 Score: {LRf1:.4f}\\n\\nClassification Report:\\n{LRreport}\"\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.text(0.01, 0.99, LR_text_output, fontsize=12, ha=\"left\", va=\"top\", family=\"monospace\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.savefig(\"LRclassification_report.png\", bbox_inches=\"tight\", dpi=300)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Confusion Matrix\n",
+    "conf_matrix = confusion_matrix(y_test, LRy_pred)\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Ham\", \"Spam\"], yticklabels=[\"Ham\", \"Spam\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.title(\"Logistic Regression Confusion Matrix\")\n",
+    "plt.savefig(\"LRconfusion_matrix.png\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model and vectorizer saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the trained model\n",
+    "joblib.dump(LRmodel, \"LRspam_classifier_model.pkl\")\n",
+    "\n",
+    "print(\"Model and vectorizer saved successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9398\n",
+      "F1 Score: 0.9375\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.89      0.99      0.94       150\n",
+      "           1       0.98      0.88      0.93       149\n",
+      "\n",
+      "    accuracy                           0.93       299\n",
+      "   macro avg       0.94      0.93      0.93       299\n",
+      "weighted avg       0.94      0.93      0.93       299\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SVM PREFORMANCE\n",
+    "SVMaccuracy = accuracy_score(y_test, SVMy_pred)\n",
+    "SVMf1 = f1_score(y_test, SVMy_pred)\n",
+    "SVM_report = classification_report(y_test, SVMy_pred)\n",
+    "\n",
+    "print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "print(f\"F1 Score: {f1:.4f}\")\n",
+    "print(\"\\nClassification Report:\\n\", SVM_report)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Save SVM report as image\n",
+    "SVM_text_output = f\"Accuracy: {SVMaccuracy:.4f}\\nF1 Score: {SVMf1:.4f}\\n\\nClassification Report:\\n{SVM_report}\"\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.text(0.01, 0.99, SVM_text_output, fontsize=12, ha=\"left\", va=\"top\", family=\"monospace\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.savefig(\"SVM_classification_report.png\", bbox_inches=\"tight\", dpi=300)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Confusion Matrix\n",
+    "conf_matrix = confusion_matrix(y_test, SVMy_pred)\n",
+    "plt.figure(figsize=(5, 4))\n",
+    "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Ham\", \"Spam\"], yticklabels=[\"Ham\", \"Spam\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.title(\"SVM Confusion Matrix\")\n",
+    "plt.savefig(\"SVMconfusion_matrix.png\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SVM model and vectorizer saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the trained model\n",
+    "joblib.dump(SVMmodel, \"SVMspam_classifier.pkl\")\n",
+    "print(\"SVM model and vectorizer saved successfully!\")"
    ]
   }
  ],