diff --git a/weather dataset/SVM.ipynb b/weather dataset/SVM.ipynb
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+++ b/weather dataset/SVM.ipynb	
@@ -0,0 +1,742 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "14347261-403d-4247-bb90-4615ba49cacf",
+   "metadata": {},
+   "source": [
+    "### *Import necessary libraries for data manipulation, preprocessing, modeling, and evaluation*\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9df35d0d-a20c-408a-85fe-b5cdd51aff11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.metrics import (\n",
+    "    accuracy_score,\n",
+    "    classification_report, \n",
+    "    confusion_matrix, \n",
+    "    ConfusionMatrixDisplay,\n",
+    "    roc_curve, \n",
+    "    auc,\n",
+    "    RocCurveDisplay\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba058d57-6a81-4af5-b468-4cbd1675f1cb",
+   "metadata": {},
+   "source": [
+    "### *Loading the diabetes dataset and display basic information about the dataset*\n",
+    "#### *The dataset is assumed to be preprocessed and saved in CSV format*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2263feea-5686-45ce-b58a-a2df5e866767",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_path = 'weather_final.csv'   \n",
+    "data = pd.read_csv(file_path)\n",
+    "\n",
+    "# Separate features and target variable\n",
+    "X = data.drop(columns=['RainTomorrow'])\n",
+    "y = data['RainTomorrow']\n",
+    "\n",
+    "\n",
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "X = imputer.fit_transform(X)\n",
+    "\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X = scaler.fit_transform(X)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e569829b-57b6-4555-96e0-4762882b7c17",
+   "metadata": {},
+   "source": [
+    "### *Spliting the dataset into features (X) and target variable (y)*\n",
+    "#### *Performing train-test split with stratification and standardize the feature data*\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ed0dbd7c-b50e-41d9-8cae-f0e3c9dc265d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into training (75%) and testing (25%) sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05213ff9-1e64-4f3c-81fe-3d52acad3211",
+   "metadata": {},
+   "source": [
+    "### *Defining the Support Vector Machine (SVM) Classifier and Setting Up the Hyperparameter Grid*\n",
+    "#### *Using GridSearchCV to Find the Best Combination of Hyperparameters*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4455c410-4a70-42ee-90c5-9d99c1111b06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\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-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 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-1 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-1 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-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"â–¸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 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-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"â–¾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 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-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=SVC(probability=True, random_state=42), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10], &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=SVC(probability=True, random_state=42), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1, 10], &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC(probability=True, random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(probability=True, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=SVC(probability=True, random_state=42), n_jobs=-1,\n",
+       "             param_grid={'C': [0.1, 1, 10], 'gamma': ['scale', 'auto'],\n",
+       "                         'kernel': ['linear', 'rbf']},\n",
+       "             scoring='accuracy')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Define the SVM classifier\n",
+    "svm = SVC(random_state=42, probability=True)\n",
+    "\n",
+    "# Define the hyperparameter grid\n",
+    "param_grid = {\n",
+    "    'C': [0.1, 1, 10],\n",
+    "    'kernel': ['linear', 'rbf'],\n",
+    "    'gamma': ['scale', 'auto']\n",
+    "}\n",
+    "\n",
+    "#  Grid Search for hyperparameter tuning\n",
+    "grid_search = GridSearchCV(svm, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
+    "grid_search.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dbfe5ef8-9841-4c17-8151-52e5edb124b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SVC(C=10, probability=True, random_state=42)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the best model\n",
+    "best_svm = grid_search.best_estimator_\n",
+    "print(best_svm)\n",
+    "# Make predictions using them\n",
+    "y_pred = best_svm.predict(X_test)\n",
+    "y_pred_proba = best_svm.predict_proba(X_test)[:, 1]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3f4506f-846c-4874-a7df-02e2d53b3fea",
+   "metadata": {},
+   "source": [
+    "### *Evaluating the best SVM model on the test set*\n",
+    "#### *Generating test accuracy, classification report.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b075e3dd-fcec-4500-9c2c-3530d634ef3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy: 0.7861034549067367\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.77      0.80      0.79      7746\n",
+      "           1       0.80      0.77      0.78      7855\n",
+      "\n",
+      "    accuracy                           0.79     15601\n",
+      "   macro avg       0.79      0.79      0.79     15601\n",
+      "weighted avg       0.79      0.79      0.79     15601\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Test Accuracy:\", accuracy_score(y_test, y_pred))\n",
+    "print(\"Classification Report:\")\n",
+    "print(classification_report(y_test, y_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42b07ed2-e3fd-4d53-80e3-b922a023f10c",
+   "metadata": {},
+   "source": [
+    "#### *Visualizing the confusion matrix using a heatmap*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d39455cd-8cf1-4412-aa7a-0c41b34a0373",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "\n",
+    "cm_display = ConfusionMatrixDisplay(\n",
+    "    confusion_matrix=confusion_matrix(y_test, y_pred),\n",
+    "    display_labels=['No Rain', 'Rain']\n",
+    ")\n",
+    "\n",
+    "cm_display.plot(ax=ax, cmap='Blues')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a1021084-4ea8-4909-9681-edf97196e182",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### *Ploting ROC Curve*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8c43a400-7471-4083-8cc0-016c1cabcecf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AUC Score: 0.861\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
+    "auc_score = auc(fpr, tpr)\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "\n",
+    "RocCurveDisplay(\n",
+    "    fpr=fpr, \n",
+    "    tpr=tpr, \n",
+    "    roc_auc=auc_score\n",
+    ").plot()\n",
+    "\n",
+    "\n",
+    "plt.title('ROC Curve')\n",
+    "plt.tight_layout()\n",
+    "print(f\"AUC Score: {auc_score:.3f}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12c61fc4-81a6-43a3-a535-8c118b9fdb5a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}