{
 "cells": [
  {
   "metadata": {
    "collapsed": true
   },
   "cell_type": "markdown",
   "source": [
    "## This is the Q2 Notebook!\n",
    "\n",
    "It's tracked via GitHub! hence the need for this line for the init commit"
   ],
   "id": "bb3519b1aa083259"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:44.706918300Z",
     "start_time": "2026-04-25T22:14:44.683287200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt"
   ],
   "id": "76e70b2ed9af0b56",
   "outputs": [],
   "execution_count": 148
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:44.729001200Z",
     "start_time": "2026-04-25T22:14:44.709018800Z"
    }
   },
   "cell_type": "code",
   "source": "df = pd.read_csv('data/googleplaystore_new_new.csv')",
   "id": "f44380615d3aba25",
   "outputs": [],
   "execution_count": 149
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Part A\n",
    "\n",
    "Q: A. Using (Rating + Reviews + Content Rating + Size in Bytes +\n",
    "Installs_Num), using Logistic regression and KNN, find and discuss the best\n",
    "classification model to predict “Category” (use the training/validation/test\n",
    "partition without cross-validation). **[8 marks]**\n"
   ],
   "id": "1baa7daa49445720"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:44.756069200Z",
     "start_time": "2026-04-25T22:14:44.729001200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "columns_to_keep = ['Rating', 'Reviews', 'Content Rating', 'Size in bytes', 'Numeric Installs', 'Category']\n",
    "df_part_a = df[columns_to_keep]"
   ],
   "id": "f6fd7137bf91f31e",
   "outputs": [],
   "execution_count": 150
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:44.886179Z",
     "start_time": "2026-04-25T22:14:44.784660200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X_raw = df_part_a.drop('Category', axis=1)\n",
    "y = df_part_a['Category']"
   ],
   "id": "b5daf475a5d5ca15",
   "outputs": [],
   "execution_count": 151
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:44.964846700Z",
     "start_time": "2026-04-25T22:14:44.888695300Z"
    }
   },
   "cell_type": "code",
   "source": "X = pd.get_dummies(X_raw, columns=['Content Rating'])",
   "id": "99a441c665dcc19b",
   "outputs": [],
   "execution_count": 152
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.068746800Z",
     "start_time": "2026-04-25T22:14:45.010546300Z"
    }
   },
   "cell_type": "code",
   "source": "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=101)",
   "id": "c6eb622c0f7ec63c",
   "outputs": [],
   "execution_count": 153
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.095344800Z",
     "start_time": "2026-04-25T22:14:45.072252400Z"
    }
   },
   "cell_type": "code",
   "source": "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.25, random_state=101)",
   "id": "89e8f117f057e0e2",
   "outputs": [],
   "execution_count": 154
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.113200900Z",
     "start_time": "2026-04-25T22:14:45.096344500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "scaler = StandardScaler()\n",
    "scaled_X_train = scaler.fit_transform(X_train)\n",
    "scaled_X_val = scaler.transform(X_val)\n",
    "scaled_X_test = scaler.transform(X_test)"
   ],
   "id": "d529991171fafb3e",
   "outputs": [],
   "execution_count": 155
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.119344300Z",
     "start_time": "2026-04-25T22:14:45.113200900Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def eval_metrics(y_true, y_pred, label=\"Model\"):\n",
    "    acc = accuracy_score(y_true, y_pred)\n",
    "    f1_w = f1_score(y_true, y_pred, average='weighted')\n",
    "    print(f\"{label} -> Accuracy: {acc:.4f} | Weighted F1: {f1_w:.4f}\")\n",
    "    return acc, f1_w"
   ],
   "id": "d44ab7b8266f66c4",
   "outputs": [],
   "execution_count": 156
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.140904500Z",
     "start_time": "2026-04-25T22:14:45.128360400Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "2f619e1e9117258e",
   "outputs": [],
   "execution_count": 156
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Logistic Regression",
   "id": "66dc17cdf1a00a06"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.162452500Z",
     "start_time": "2026-04-25T22:14:45.142480200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "log_model = LogisticRegression(max_iter=1000, random_state=101)\n",
    "log_model.fit(scaled_X_train, y_train)"
   ],
   "id": "84128fa4e7823565",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=101)"
      ],
      "text/html": [
       "<style>#sk-container-id-14 {\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-container-id-14.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-container-id-14.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-container-id-14 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 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-14 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-14 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-14 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-14 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-14 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-14 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-14 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-14 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-14 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-container-id-14 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-container-id-14 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-14 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-container-id-14 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 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-14 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 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-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-14 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-14 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-14 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-14 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-14 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-14 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-14 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-14 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-14 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-14 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-14 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-14 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 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-container-id-14 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-container-id-14 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-container-id-14 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-14 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\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",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\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",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\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",
       "/* \"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",
       "/* 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",
       "</style><body><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=101)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></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=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</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('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">101</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1000</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",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
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       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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     "execution_count": 157,
     "metadata": {},
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   "execution_count": 157
  },
  {
   "metadata": {
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    }
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   "cell_type": "code",
   "source": [
    "y_val_pred_log = log_model.predict(scaled_X_val)\n",
    "log_val_acc, log_val_f1 = eval_metrics(y_val, y_val_pred_log, \"LogReg (Validation)\")"
   ],
   "id": "965e16ce48443c97",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg (Validation) -> Accuracy: 0.3333 | Weighted F1: 0.3069\n"
     ]
    }
   ],
   "execution_count": 158
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.228185100Z",
     "start_time": "2026-04-25T22:14:45.182368500Z"
    }
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   "cell_type": "code",
   "source": [
    "y_test_pred_log = log_model.predict(scaled_X_test)\n",
    "log_test_acc, log_test_f1 = eval_metrics(y_test, y_test_pred_log, \"LogReg (Test)\")"
   ],
   "id": "9be803af2f34cd6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg (Test) -> Accuracy: 0.3108 | Weighted F1: 0.2646\n"
     ]
    }
   ],
   "execution_count": 159
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.239724800Z",
     "start_time": "2026-04-25T22:14:45.229190800Z"
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   "cell_type": "code",
   "source": [
    "log_probs = log_model.predict_proba(scaled_X_test)\n",
    "log_pred_idx = np.argmax(log_probs, axis=1)\n",
    "log_pred_conf = log_probs[np.arange(len(log_probs)), log_pred_idx]\n",
    "\n",
    "log_pred_df = pd.DataFrame({\n",
    "    \"True_Label\": y_test.reset_index(drop=True),\n",
    "    \"Predicted_Label\": pd.Series(y_test_pred_log).reset_index(drop=True),\n",
    "    \"Predicted_Confidence\": log_pred_conf\n",
    "})\n",
    "log_pred_df[\"Correct\"] = log_pred_df[\"True_Label\"] == log_pred_df[\"Predicted_Label\"]\n",
    "\n",
    "display(log_pred_df.head(15))"
   ],
   "id": "6a22b4c9c5a0876a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            True_Label     Predicted_Label  Predicted_Confidence  Correct\n",
       "0              FINANCE             FINANCE              0.201206     True\n",
       "1        COMMUNICATION             FINANCE              0.127063    False\n",
       "2   LIBRARIES_AND_DEMO           EDUCATION              0.134790    False\n",
       "3            LIFESTYLE           EDUCATION              0.146064    False\n",
       "4            EDUCATION           EDUCATION              0.136238     True\n",
       "5       ART_AND_DESIGN           LIFESTYLE              0.143387    False\n",
       "6               BEAUTY           EDUCATION              0.125436    False\n",
       "7               DATING              DATING              0.865158     True\n",
       "8       HOUSE_AND_HOME           LIFESTYLE              0.120546    False\n",
       "9              FINANCE  HEALTH_AND_FITNESS              0.274936    False\n",
       "10  HEALTH_AND_FITNESS                GAME              0.407868    False\n",
       "11           EDUCATION           EDUCATION              0.143047     True\n",
       "12  HEALTH_AND_FITNESS  HEALTH_AND_FITNESS              0.198159     True\n",
       "13             FINANCE           EDUCATION              0.136472    False\n",
       "14             FINANCE             FINANCE              0.180731     True"
      ],
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       "      <th></th>\n",
       "      <th>True_Label</th>\n",
       "      <th>Predicted_Label</th>\n",
       "      <th>Predicted_Confidence</th>\n",
       "      <th>Correct</th>\n",
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       "      <td>COMMUNICATION</td>\n",
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       "      <td>0.127063</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>HEALTH_AND_FITNESS</td>\n",
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       "      <td>0.198159</td>\n",
       "      <td>True</td>\n",
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       "      <td>FINANCE</td>\n",
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   "execution_count": 160
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## KNN",
   "id": "2b7b85284cddecb2"
  },
  {
   "metadata": {
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   "cell_type": "code",
   "source": [
    "best_k = 1\n",
    "best_f1 = 0.0"
   ],
   "id": "8c4835d7a413a6e6",
   "outputs": [],
   "execution_count": 161
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.493811800Z",
     "start_time": "2026-04-25T22:14:45.260308900Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test_error_rates = []\n",
    "k_candidates = list(range(1, 30))  # 1..29\n",
    "\n",
    "for k in k_candidates:\n",
    "    knn_model = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn_model.fit(scaled_X_train, y_train)\n",
    "    y_pred_test_k = knn_model.predict(scaled_X_test)\n",
    "\n",
    "    # Elbow metric based on test error (as requested)\n",
    "    test_error = 1 - accuracy_score(y_test, y_pred_test_k)\n",
    "    test_error_rates.append(test_error)\n",
    "\n",
    "    # Keep track of best k by validation weighted F1 (good model selection practice)\n",
    "    y_pred_val_k = knn_model.predict(scaled_X_val)\n",
    "    current_f1 = f1_score(y_val, y_pred_val_k, average='weighted')\n",
    "    if current_f1 > best_f1:\n",
    "        best_f1 = current_f1\n",
    "        best_k = k"
   ],
   "id": "a8d69c56d1a21825",
   "outputs": [],
   "execution_count": 162
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.553707200Z",
     "start_time": "2026-04-25T22:14:45.494813Z"
    }
   },
   "cell_type": "code",
   "source": [
    "knn_probs = knn_final.predict_proba(scaled_X_test)\n",
    "knn_pred_idx = np.argmax(knn_probs, axis=1)\n",
    "knn_pred_conf = knn_probs[np.arange(len(knn_probs)), knn_pred_idx]\n",
    "\n",
    "knn_pred_df = pd.DataFrame({\n",
    "    \"True_Label\": y_test.reset_index(drop=True),\n",
    "    \"Predicted_Label\": pd.Series(y_test_pred_knn).reset_index(drop=True),\n",
    "    \"Predicted_Confidence\": knn_pred_conf\n",
    "})\n",
    "knn_pred_df[\"Correct\"] = knn_pred_df[\"True_Label\"] == knn_pred_df[\"Predicted_Label\"]\n",
    "\n",
    "display(knn_pred_df.head(15))"
   ],
   "id": "80bea830703c3c4b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            True_Label     Predicted_Label  Predicted_Confidence  Correct\n",
       "0              FINANCE              BEAUTY              0.333333    False\n",
       "1        COMMUNICATION      ART_AND_DESIGN              0.333333    False\n",
       "2   LIBRARIES_AND_DEMO       COMMUNICATION              0.333333    False\n",
       "3            LIFESTYLE            BUSINESS              0.333333    False\n",
       "4            EDUCATION                GAME              0.666667    False\n",
       "5       ART_AND_DESIGN   AUTO_AND_VEHICLES              0.333333    False\n",
       "6               BEAUTY              BEAUTY              0.333333     True\n",
       "7               DATING              DATING              1.000000     True\n",
       "8       HOUSE_AND_HOME   AUTO_AND_VEHICLES              0.333333    False\n",
       "9              FINANCE  HEALTH_AND_FITNESS              0.666667    False\n",
       "10  HEALTH_AND_FITNESS             FINANCE              0.333333    False\n",
       "11           EDUCATION  HEALTH_AND_FITNESS              0.666667    False\n",
       "12  HEALTH_AND_FITNESS           EDUCATION              0.666667    False\n",
       "13             FINANCE      ART_AND_DESIGN              0.333333    False\n",
       "14             FINANCE             FINANCE              1.000000     True"
      ],
      "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>True_Label</th>\n",
       "      <th>Predicted_Label</th>\n",
       "      <th>Predicted_Confidence</th>\n",
       "      <th>Correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LIBRARIES_AND_DEMO</td>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>GAME</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>AUTO_AND_VEHICLES</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DATING</td>\n",
       "      <td>DATING</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HOUSE_AND_HOME</td>\n",
       "      <td>AUTO_AND_VEHICLES</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 163
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.662352Z",
     "start_time": "2026-04-25T22:14:45.555211200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "best_acc_idx = int(np.argmax(knn_val_acc_scores))\n",
    "best_f1_idx = int(np.argmax(knn_val_f1_scores))\n",
    "\n",
    "plt.figure(figsize=(9, 5))\n",
    "plt.plot(k_values, knn_val_acc_scores, marker='o', linewidth=2, label='Validation Accuracy')\n",
    "plt.plot(k_values, knn_val_f1_scores, marker='s', linewidth=2, label='Validation Weighted F1')\n",
    "\n",
    "# Highlight best metric points\n",
    "plt.scatter(\n",
    "    k_values[best_acc_idx],\n",
    "    knn_val_acc_scores[best_acc_idx],\n",
    "    s=120,\n",
    "    color='green',\n",
    "    zorder=5,\n",
    "    label=f'Best Accuracy (k={k_values[best_acc_idx]})'\n",
    ")\n",
    "plt.scatter(\n",
    "    k_values[best_f1_idx],\n",
    "    knn_val_f1_scores[best_f1_idx],\n",
    "    s=120,\n",
    "    color='red',\n",
    "    zorder=5,\n",
    "    label=f'Best Weighted F1 (k={k_values[best_f1_idx]})'\n",
    ")\n",
    "\n",
    "plt.axvline(best_k, color='red', linestyle='--', alpha=0.5, label=f'Selected k = {best_k}')\n",
    "\n",
    "plt.title('KNN Validation Performance vs K (Dynamic)')\n",
    "plt.xlabel('Number of Neighbors (k)')\n",
    "plt.ylabel('Score')\n",
    "plt.xticks(k_values)\n",
    "plt.grid(alpha=0.3)\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "4b317d655f54f88b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 164
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.740796100Z",
     "start_time": "2026-04-25T22:14:45.663357500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 6), dpi=120)\n",
    "plt.plot(k_candidates, test_error_rates, label='test error')\n",
    "plt.xlabel('k value')\n",
    "plt.title('Elbow method for KNN')\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ],
   "id": "8206b3ab050f67e9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x720 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 165
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.780483600Z",
     "start_time": "2026-04-25T22:14:45.741796700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "knn_final = KNeighborsClassifier(n_neighbors=best_k)\n",
    "knn_final.fit(scaled_X_train, y_train)"
   ],
   "id": "a0f18919585864e0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ],
      "text/html": [
       "<style>#sk-container-id-15 {\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-container-id-15.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-container-id-15.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-container-id-15 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 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-15 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-15 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-15 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-15 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-15 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-15 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-15 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-15 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-15 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-container-id-15 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-container-id-15 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-15 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-container-id-15 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 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-15 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 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-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-15 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-15 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-15 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-15 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-15 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-15 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-15 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-15 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-15 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-15 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-15 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-15 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-15 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-15 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-container-id-15 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-container-id-15 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-container-id-15 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-15 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\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",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\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",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\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",
       "/* \"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",
       "/* 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",
       "</style><body><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" 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/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</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('n_neighbors',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
       "            n_neighbors\n",
       "            <span class=\"param-doc-description\">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\">3</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",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/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\">weights: {'uniform', 'distance'}, callable or None, default='uniform'<br><br>Weight function used in prediction.  Possible values:<br><br>- 'uniform' : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- 'distance' : 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",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/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\">algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'<br><br>Algorithm used to compute the nearest neighbors:<br><br>- 'ball_tree' will use :class:`BallTree`<br>- 'kd_tree' will use :class:`KDTree`<br>- 'brute' will use a brute-force search.<br>- 'auto' 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",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
       "            leaf_size\n",
       "            <span class=\"param-doc-description\">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",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
       "            p\n",
       "            <span class=\"param-doc-description\">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",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
       "            metric\n",
       "            <span class=\"param-doc-description\">metric: str or callable, default='minkowski'<br><br>Metric to use for distance computation. Default is \"minkowski\", which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br><https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is \"precomputed\", 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 \"nonzero\" 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'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",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
       "            metric_params\n",
       "            <span class=\"param-doc-description\">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",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">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 <n_jobs>`<br>for more details.<br>Doesn'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",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 166
  },
  {
   "metadata": {
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     "end_time": "2026-04-25T22:14:45.854662300Z",
     "start_time": "2026-04-25T22:14:45.804657700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "y_val_pred_knn = knn_final.predict(scaled_X_val)\n",
    "knn_val_acc, knn_val_f1 = eval_metrics(y_val, y_val_pred_knn, f\"KNN k={best_k} (Validation)\")"
   ],
   "id": "fff19d47a4e5d268",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN k=3 (Validation) -> Accuracy: 0.3288 | Weighted F1: 0.3245\n"
     ]
    }
   ],
   "execution_count": 167
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:45.958387Z",
     "start_time": "2026-04-25T22:14:45.887783700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "y_test_pred_knn = knn_final.predict(scaled_X_test)\n",
    "knn_test_acc, knn_test_f1 = eval_metrics(y_test, y_test_pred_knn, f\"KNN k={best_k} (Test)\")"
   ],
   "id": "7bca71dd45a3207e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN k=3 (Test) -> Accuracy: 0.2523 | Weighted F1: 0.2288\n"
     ]
    }
   ],
   "execution_count": 168
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  {
   "metadata": {
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   "cell_type": "code",
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    "results_df = pd.DataFrame([\n",
    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Validation\", \"Accuracy\": log_val_acc, \"Weighted F1\": log_val_f1},\n",
    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Test\",       \"Accuracy\": log_test_acc, \"Weighted F1\": log_test_f1},\n",
    "    {\"Model\": f\"KNN (k={best_k})\",   \"Split\": \"Validation\", \"Accuracy\": knn_val_acc, \"Weighted F1\": knn_val_f1},\n",
    "    {\"Model\": f\"KNN (k={best_k})\",   \"Split\": \"Test\",       \"Accuracy\": knn_test_acc, \"Weighted F1\": knn_test_f1},\n",
    "])\n",
    "\n",
    "results_df"
   ],
   "id": "36c97146b1995aaf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 Model       Split  Accuracy  Weighted F1\n",
       "0  Logistic Regression  Validation  0.333333     0.306923\n",
       "1  Logistic Regression        Test  0.310811     0.264609\n",
       "2            KNN (k=3)  Validation  0.328829     0.324541\n",
       "3            KNN (k=3)        Test  0.252252     0.228784"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Split</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Weighted F1</th>\n",
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       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
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       "      <td>0.333333</td>\n",
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       "      <td>Logistic Regression</td>\n",
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       "      <td>0.310811</td>\n",
       "      <td>0.264609</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN (k=3)</td>\n",
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       "      <td>0.328829</td>\n",
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   },
   "cell_type": "code",
   "source": [
    "top_n = 10\n",
    "top_classes = y_test.value_counts().head(top_n).index.tolist()\n",
    "\n",
    "mask = y_test.isin(top_classes)\n",
    "y_test_top = y_test[mask]\n",
    "y_pred_top = pd.Series(y_test_pred_knn, index=y_test.index)[mask]\n",
    "\n",
    "cm_top = confusion_matrix(y_test_top, y_pred_top, labels=top_classes)\n",
    "disp_top = ConfusionMatrixDisplay(confusion_matrix=cm_top, display_labels=top_classes)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "disp_top.plot(\n",
    "    ax=ax,\n",
    "    cmap='viridis',\n",
    "    xticks_rotation=45,\n",
    "    values_format='d',\n",
    "    colorbar=False\n",
    ")\n",
    "ax.set_title(f'KNN Confusion Matrix (Top {top_n} Classes, k={best_k})')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "5e54ea3373642e04",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 170
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Part B\n",
    "\n",
    "Q: Using (Rating + Reviews + Category + Size in Bytes + Installs_Num),\n",
    "using Logistic regression and KNN, find and discuss the best classification model\n",
    "to predict “Content Rating” (use the training/validation/test partition without\n",
    "cross-validation). **[7 marks]**"
   ],
   "id": "f15aa6cca58826fc"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:46.190884Z",
     "start_time": "2026-04-25T22:14:46.169566800Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "65e55b70ad44b2dc",
   "outputs": [],
   "execution_count": 170
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Part C\n",
    "\n",
    "Q: By considering Installs_Num as categorical feature and using (Rating +\n",
    "Reviews + Category + Size in Bytes + Content Rating), find and discuss the\n",
    "best classification model to predict “Installs_Num” (using Logistic regression and\n",
    "KNN) (use the training/test partition without cross-validation) **[8 marks]**"
   ],
   "id": "86c040f5e043a39c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-25T22:14:46.205129700Z",
     "start_time": "2026-04-25T22:14:46.192396600Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "f3535461938f94f5",
   "outputs": [],
   "execution_count": 170
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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