{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "01597606",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d49c426",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0  15624510    Male   19            19000          0\n",
       "1  15810944    Male   35            20000          0\n",
       "2  15668575  Female   26            43000          0\n",
       "3  15603246  Female   27            57000          0\n",
       "4  15804002    Male   19            76000          0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the Social Network Ads dataset\n",
    "data = pd.read_csv(\"Social_Network_Ads.csv\")\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb4fab12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: ['Age', 'EstimatedSalary', 'Gender_Male']\n",
      "Target classes: [0, 1]\n"
     ]
    }
   ],
   "source": [
    "# Prepare features and target\n",
    "data = data.drop(columns=[\"User ID\"], errors=\"ignore\")\n",
    "data = pd.get_dummies(data, columns=[\"Gender\"], drop_first=True)\n",
    "\n",
    "X = data.drop(columns=[\"Purchased\"])\n",
    "y = data[\"Purchased\"]\n",
    "\n",
    "print(\"Features:\", X.columns.tolist())\n",
    "print(\"Target classes:\", sorted(y.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "371530b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.25, random_state=42, stratify=y\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ee10a92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global 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-global 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-global 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-global 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-global 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-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global 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-global 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-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global 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-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global 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-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global 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-global 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-global 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-global 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-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global 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-global 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-global 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-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global 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-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global 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-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\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",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\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",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\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",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\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-global 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-unfitted-level-0);\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-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\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-global 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-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
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       "\n",
       ".estimator-table details[open] {\n",
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       "    padding-bottom: 0.3rem;\n",
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       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
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       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
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       "\n",
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       "    background-color: #f6f6f6;\n",
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       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
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       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
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       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
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       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
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       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kneighborsclassifier&#x27;, KNeighborsClassifier())])</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 sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-steps;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
       "            steps\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-steps;\">\n",
       "            steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators &lt;combining_estimators&gt;` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;standardscaler&#x27;, ...), (&#x27;kneighborsclassifier&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transform_input',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-transform_input;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
       "            transform_input\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-transform_input;\">\n",
       "            transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing &lt;metadata_routing&gt;`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('memory',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-memory;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
       "            memory\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-memory;\">\n",
       "            memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes,)<br><br>The classes labels. Only exist if the last step of the pipeline is a<br>classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
       "            feature_names_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Only defined if the<br>underlying estimator exposes such an attribute when fit.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](3,)</td>\n",
       "           <td>[&#x27;Age&#x27;,&#x27;EstimatedSalary&#x27;,&#x27;Gender_Male&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`. Only defined if the<br>underlying first estimator in `steps` exposes such an attribute<br>when fit.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>3</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </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 sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"standardscaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_mean;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_mean;\">\n",
       "            with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_std;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_std;\">\n",
       "            with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
       "            feature_names_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Defined only when `X`<br>has feature names that are all strings.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](3,)</td>\n",
       "           <td>[&#x27;Age&#x27;,&#x27;EstimatedSalary&#x27;,&#x27;Gender_Male&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-mean_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=mean_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            mean_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-mean_;\">\n",
       "            mean_: ndarray of shape (n_features,) or None<br><br>The mean value for each feature in the training set.<br>Equal to ``None`` when ``with_mean=False`` and ``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
       "           <td>[   37.14,70903.33,    0.46]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>3</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_seen_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_samples_seen_,-int%20or%20ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            n_samples_seen_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_seen_;\">\n",
       "            n_samples_seen_: int or ndarray of shape (n_features,)<br><br>The number of samples processed by the estimator for each feature.<br>If there are no missing samples, the ``n_samples_seen`` will be an<br>integer, otherwise it will be an array of dtype int. If<br>`sample_weights` are used it will be a float (if no missing data)<br>or an array of dtype float that sums the weights seen so far.<br>Will be reset on new calls to fit, but increments across<br>``partial_fit`` calls.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>300</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scale_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=scale_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            scale_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scale_;\">\n",
       "            scale_: ndarray of shape (n_features,) or None<br><br>Per feature relative scaling of the data to achieve zero mean and unit<br>variance. Generally this is calculated using `np.sqrt(var_)`. If a<br>variance is zero, we can&#x27;t achieve unit variance, and the data is left<br>as-is, giving a scaling factor of 1. `scale_` is equal to `None`<br>when `with_std=False`.<br><br>.. versionadded:: 0.17<br>   *scale_*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
       "           <td>[   10.61,35162.96,    0.5 ]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-var_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=var_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            var_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-var_;\">\n",
       "            var_: ndarray of shape (n_features,) or None<br><br>The variance for each feature in the training set. Used to compute<br>`scale_`. Equal to ``None`` when ``with_mean=False`` and<br>``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
       "           <td>[1.13e+02,1.24e+09,2.48e-01]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>3 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>Age</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>EstimatedSalary</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>Gender_Male</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"kneighborsclassifier__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_neighbors',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_neighbors;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
       "            n_neighbors\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_neighbors;\">\n",
       "            n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-weights;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
       "            weights\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-weights;\">\n",
       "            weights: {&#x27;uniform&#x27;, &#x27;distance&#x27;}, callable or None, default=&#x27;uniform&#x27;<br><br>Weight function used in prediction.  Possible values:<br><br>- &#x27;uniform&#x27; : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- &#x27;distance&#x27; : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-algorithm;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-algorithm;\">\n",
       "            algorithm: {&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;}, default=&#x27;auto&#x27;<br><br>Algorithm used to compute the nearest neighbors:<br><br>- &#x27;ball_tree&#x27; will use :class:`BallTree`<br>- &#x27;kd_tree&#x27; will use :class:`KDTree`<br>- &#x27;brute&#x27; will use a brute-force search.<br>- &#x27;auto&#x27; will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('leaf_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-leaf_size;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
       "            leaf_size\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-leaf_size;\">\n",
       "            leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">30</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('p',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-p;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
       "            p\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-p;\">\n",
       "            p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
       "            metric\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric;\">\n",
       "            metric: str or callable, default=&#x27;minkowski&#x27;<br><br>Metric to use for distance computation. Default is &quot;minkowski&quot;, which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br>&lt;https://docs.scipy.org/doc/scipy/reference/spatial.distance.html&gt;`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is &quot;precomputed&quot;, X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only &quot;nonzero&quot; elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy&#x27;s metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric_params',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric_params;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
       "            metric_params\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric_params;\">\n",
       "            metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.<br>Doesn&#x27;t affect :meth:`fit` method.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=classes_,-array%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: array of shape (n_classes,)<br><br>Class labels known to the classifier</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_,-str%20or%20callble\">\n",
       "            effective_metric_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_;\">\n",
       "            effective_metric_: str or callble<br><br>The distance metric used. It will be same as the `metric` parameter<br>or a synonym of it, e.g. &#x27;euclidean&#x27; if the `metric` parameter set to<br>&#x27;minkowski&#x27; and `p` parameter set to 2.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">str</td>\n",
       "           <td>&#x27;eu...an&#x27;</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_params_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_params_,-dict\">\n",
       "            effective_metric_params_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_params_;\">\n",
       "            effective_metric_params_: dict<br><br>Additional keyword arguments for the metric function. For most metrics<br>will be same with `metric_params` parameter, but may also contain the<br>`p` parameter value if the `effective_metric_` attribute is set to<br>&#x27;minkowski&#x27;.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>3</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_fit_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_samples_fit_,-int\">\n",
       "            n_samples_fit_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_fit_;\">\n",
       "            n_samples_fit_: int<br><br>Number of samples in the fitted data.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>300</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-outputs_2d_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=outputs_2d_,-bool\">\n",
       "            outputs_2d_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-outputs_2d_;\">\n",
       "            outputs_2d_: bool<br><br>False when `y`&#x27;s shape is (n_samples, ) or (n_samples, 1) during fit<br>otherwise True.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('kneighborsclassifier', KNeighborsClassifier())])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale the features and train the KNN classifier\n",
    "knn_model = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    KNeighborsClassifier(n_neighbors=5)\n",
    ")\n",
    "knn_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb5eda19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict whether each user will purchase\n",
    "y_pred = knn_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39505c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[59  5]\n",
      " [ 4 32]]\n",
      "Accuracy: 0.91\n",
      "Error Rate: 0.09\n",
      "Precision: 0.8649\n",
      "Recall: 0.8889\n"
     ]
    }
   ],
   "source": [
    "# Calculate confusion matrix and evaluation metrics\n",
    "cm = confusion_matrix(y_test, y_pred, labels=[0, 1])\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "error_rate = 1 - accuracy\n",
    "precision = precision_score(y_test, y_pred, zero_division=0)\n",
    "recall = recall_score(y_test, y_pred, zero_division=0)\n",
    "\n",
    "print(\"Confusion Matrix:\\n\", cm)\n",
    "print(\"Accuracy:\", round(accuracy, 4))\n",
    "print(\"Error Rate:\", round(error_rate, 4))\n",
    "print(\"Precision:\", round(precision, 4))\n",
    "print(\"Recall:\", round(recall, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04bd639e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the confusion matrix\n",
    "ConfusionMatrixDisplay(\n",
    "    confusion_matrix=cm,\n",
    "    display_labels=[\"Not Purchased\", \"Purchased\"]\n",
    ").plot(cmap=\"Blues\")\n",
    "\n",
    "plt.title(\"KNN Confusion Matrix\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}