{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb3519b1aa083259",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## This is the Q2 Notebook!\n",
    "\n",
    "It's tracked via GitHub! hence the need for this line for the init commit"
   ]
  },
  {
   "cell_type": "code",
   "id": "76e70b2ed9af0b56",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.055434300Z",
     "start_time": "2026-04-26T14:22:29.010947900Z"
    }
   },
   "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, confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 67
  },
  {
   "cell_type": "code",
   "id": "4a05800d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.119438800Z",
     "start_time": "2026-04-26T14:22:29.079996400Z"
    }
   },
   "source": [
    "# Keep one fixed seed so your splits and metrics are stable each run.\n",
    "seed = 101"
   ],
   "outputs": [],
   "execution_count": 68
  },
  {
   "cell_type": "code",
   "id": "f44380615d3aba25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.148305400Z",
     "start_time": "2026-04-26T14:22:29.122438300Z"
    }
   },
   "source": [
    "df = pd.read_csv('data/googleplaystore_new_new.csv')"
   ],
   "outputs": [],
   "execution_count": 69
  },
  {
   "cell_type": "markdown",
   "id": "1baa7daa49445720",
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "id": "f6fd7137bf91f31e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.200889200Z",
     "start_time": "2026-04-26T14:22:29.170323700Z"
    }
   },
   "source": [
    "columns_to_keep = ['Rating', 'Reviews', 'Content Rating', 'Size in bytes', 'Numeric Installs', 'Category']\n",
    "df_part_a = df[columns_to_keep]"
   ],
   "outputs": [],
   "execution_count": 70
  },
  {
   "cell_type": "code",
   "id": "b5daf475a5d5ca15",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.231293400Z",
     "start_time": "2026-04-26T14:22:29.201895100Z"
    }
   },
   "source": [
    "X_raw = df_part_a.drop('Category', axis=1)\n",
    "y = df_part_a['Category']"
   ],
   "outputs": [],
   "execution_count": 71
  },
  {
   "cell_type": "code",
   "id": "99a441c665dcc19b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.285866900Z",
     "start_time": "2026-04-26T14:22:29.232290500Z"
    }
   },
   "source": [
    "X = pd.get_dummies(X_raw, columns=['Content Rating'])"
   ],
   "outputs": [],
   "execution_count": 72
  },
  {
   "cell_type": "code",
   "id": "c6eb622c0f7ec63c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.328943200Z",
     "start_time": "2026-04-26T14:22:29.288372900Z"
    }
   },
   "source": [
    "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)"
   ],
   "outputs": [],
   "execution_count": 73
  },
  {
   "cell_type": "code",
   "id": "89e8f117f057e0e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.372324200Z",
     "start_time": "2026-04-26T14:22:29.330948900Z"
    }
   },
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.25, random_state=seed)"
   ],
   "outputs": [],
   "execution_count": 74
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.426224200Z",
     "start_time": "2026-04-26T14:22:29.374321200Z"
    }
   },
   "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": "b9c0f0dcdb1e5097",
   "outputs": [],
   "execution_count": 75
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.456903100Z",
     "start_time": "2026-04-26T14:22:29.427224100Z"
    }
   },
   "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",
    "    return acc, f1_w\n",
    "\n",
    "\n",
    "def split_overview(train_rows, test_rows, val_rows=None):\n",
    "    rows = [\n",
    "        {'Split': 'Train', 'Rows': train_rows},\n",
    "        {'Split': 'Test', 'Rows': test_rows},\n",
    "    ]\n",
    "    if val_rows is not None:\n",
    "        rows.insert(1, {'Split': 'Validation', 'Rows': val_rows})\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "\n",
    "def prediction_sheet(y_true, y_pred, y_prob, prefix, head_n=20):\n",
    "    pred_idx = np.argmax(y_prob, axis=1)\n",
    "    pred_conf = y_prob[np.arange(len(y_prob)), pred_idx]\n",
    "    sheet = pd.DataFrame({\n",
    "        f'True_{prefix}': pd.Series(y_true).reset_index(drop=True),\n",
    "        f'Predicted_{prefix}': pd.Series(y_pred).reset_index(drop=True),\n",
    "        'Predicted_Confidence': pred_conf\n",
    "    })\n",
    "    sheet['Correct'] = sheet[f'True_{prefix}'] == sheet[f'Predicted_{prefix}']\n",
    "    return sheet.head(head_n)"
   ],
   "id": "930d2242b3f0683d",
   "outputs": [],
   "execution_count": 76
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.544499600Z",
     "start_time": "2026-04-26T14:22:29.484412800Z"
    }
   },
   "cell_type": "code",
   "source": "split_overview(len(X_train), len(X_test), len(X_val))",
   "id": "1a3a319e65a8a24b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        Split  Rows\n",
       "0       Train   663\n",
       "1  Validation   222\n",
       "2        Test   222"
      ],
      "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>Split</th>\n",
       "      <th>Rows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Validation</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Test</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 77
  },
  {
   "cell_type": "code",
   "id": "2f619e1e9117258e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.601534800Z",
     "start_time": "2026-04-26T14:22:29.546506800Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": 77
  },
  {
   "cell_type": "markdown",
   "id": "66dc17cdf1a00a06",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "id": "84128fa4e7823565",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.657949200Z",
     "start_time": "2026-04-26T14:22:29.603533700Z"
    }
   },
   "source": [
    "# Logistic Regression (tuned a bit for better performance on imbalanced classes)\n",
    "log_model = LogisticRegression(\n",
    "    max_iter=5000,\n",
    "    random_state=seed,\n",
    "    class_weight='balanced',\n",
    "    C=2.0,\n",
    "    solver='lbfgs'\n",
    ")\n",
    "log_model.fit(scaled_X_train, y_train)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=2.0, class_weight='balanced', max_iter=5000,\n",
       "                   random_state=101)"
      ],
      "text/html": [
       "<style>#sk-container-id-3 {\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-3.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-3.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-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=2.0, class_weight=&#x27;balanced&#x27;, max_iter=5000,\n",
       "                   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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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=\"user-set\">\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\">2.0</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=\"user-set\">\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\">&#x27;balanced&#x27;</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\">5000</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",
       "            <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.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",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-3');</script></body>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 78
  },
  {
   "cell_type": "code",
   "id": "965e16ce48443c97",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.693126Z",
     "start_time": "2026-04-26T14:22:29.664952800Z"
    }
   },
   "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)\")"
   ],
   "outputs": [],
   "execution_count": 79
  },
  {
   "cell_type": "code",
   "id": "9be803af2f34cd6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.738350400Z",
     "start_time": "2026-04-26T14:22:29.694126100Z"
    }
   },
   "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)\")"
   ],
   "outputs": [],
   "execution_count": 80
  },
  {
   "cell_type": "code",
   "id": "6a22b4c9c5a0876a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:29.767862Z",
     "start_time": "2026-04-26T14:22:29.739348Z"
    }
   },
   "source": [
    "log_probs = log_model.predict_proba(scaled_X_test)\n",
    "log_pred_df = prediction_sheet(y_test, y_test_pred_log, log_probs, 'Category', head_n=15)\n",
    "log_pred_df"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         True_Category  Predicted_Category  Predicted_Confidence  Correct\n",
       "0              FINANCE             FINANCE              0.144609     True\n",
       "1        COMMUNICATION            BUSINESS              0.094501    False\n",
       "2   LIBRARIES_AND_DEMO              BEAUTY              0.086577    False\n",
       "3            LIFESTYLE              EVENTS              0.105029    False\n",
       "4            EDUCATION              EVENTS              0.087598    False\n",
       "5       ART_AND_DESIGN      HOUSE_AND_HOME              0.189590    False\n",
       "6               BEAUTY      ART_AND_DESIGN              0.102613    False\n",
       "7               DATING              DATING              0.699165     True\n",
       "8       HOUSE_AND_HOME      HOUSE_AND_HOME              0.131807     True\n",
       "9              FINANCE  HEALTH_AND_FITNESS              0.183727    False\n",
       "10  HEALTH_AND_FITNESS                GAME              0.348999    False\n",
       "11           EDUCATION      ART_AND_DESIGN              0.112001    False\n",
       "12  HEALTH_AND_FITNESS  HEALTH_AND_FITNESS              0.118400     True\n",
       "13             FINANCE              BEAUTY              0.088732    False\n",
       "14             FINANCE             FINANCE              0.124280     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_Category</th>\n",
       "      <th>Predicted_Category</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>FINANCE</td>\n",
       "      <td>0.144609</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>0.094501</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LIBRARIES_AND_DEMO</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.086577</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>EVENTS</td>\n",
       "      <td>0.105029</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>EVENTS</td>\n",
       "      <td>0.087598</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>HOUSE_AND_HOME</td>\n",
       "      <td>0.189590</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>0.102613</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DATING</td>\n",
       "      <td>DATING</td>\n",
       "      <td>0.699165</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HOUSE_AND_HOME</td>\n",
       "      <td>HOUSE_AND_HOME</td>\n",
       "      <td>0.131807</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>0.183727</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>GAME</td>\n",
       "      <td>0.348999</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>0.112001</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>0.118400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.088732</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>0.124280</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 81
  },
  {
   "cell_type": "markdown",
   "id": "2b7b85284cddecb2",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "id": "a8d69c56d1a21825",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:30.447555200Z",
     "start_time": "2026-04-26T14:22:29.769862300Z"
    }
   },
   "source": [
    "best_k = 1\n",
    "best_f1 = 0.0\n",
    "\n",
    "test_error_rates = []\n",
    "knn_val_acc_scores = []\n",
    "knn_val_f1_scores = []\n",
    "\n",
    "k_candidates = list(range(1, 51))  # 1..50\n",
    "\n",
    "for k in k_candidates:\n",
    "    knn_model = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
    "    knn_model.fit(scaled_X_train, y_train)\n",
    "\n",
    "    # test error (for elbow)\n",
    "    y_pred_test_k = knn_model.predict(scaled_X_test)\n",
    "    test_error_rates.append(1 - accuracy_score(y_test, y_pred_test_k))\n",
    "\n",
    "    # validation metrics (for selection + plot)\n",
    "    y_pred_val_k = knn_model.predict(scaled_X_val)\n",
    "    val_acc_k = accuracy_score(y_val, y_pred_val_k)\n",
    "    val_f1_k = f1_score(y_val, y_pred_val_k, average='weighted')\n",
    "\n",
    "    knn_val_acc_scores.append(val_acc_k)\n",
    "    knn_val_f1_scores.append(val_f1_k)\n",
    "\n",
    "    if val_f1_k > best_f1:\n",
    "        best_f1 = val_f1_k\n",
    "        best_k = k"
   ],
   "outputs": [],
   "execution_count": 82
  },
  {
   "cell_type": "code",
   "id": "8206b3ab050f67e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:30.632246600Z",
     "start_time": "2026-04-26T14:22:30.551913300Z"
    }
   },
   "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()"
   ],
   "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": 83
  },
  {
   "cell_type": "code",
   "id": "a0f18919585864e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:30.686659800Z",
     "start_time": "2026-04-26T14:22:30.633246300Z"
    }
   },
   "source": [
    "knn_final = KNeighborsClassifier(n_neighbors=best_k, weights='distance')\n",
    "knn_final.fit(scaled_X_train, y_train)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=19, weights='distance')"
      ],
      "text/html": [
       "<style>#sk-container-id-4 {\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-4.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-4.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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=19, weights=&#x27;distance&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" 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\">19</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\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;distance&#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",
       "    // Get the parameter prefix from the closest toggleable content\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
       "\n",
       "    const originalStyle = element.style;\n",
       "    const computedStyle = window.getComputedStyle(element);\n",
       "    const originalWidth = computedStyle.width;\n",
       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
       "\n",
       "    navigator.clipboard.writeText(fullParamName)\n",
       "        .then(() => {\n",
       "            element.style.width = originalWidth;\n",
       "            element.style.color = 'green';\n",
       "            element.innerHTML = \"Copied!\";\n",
       "\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        })\n",
       "        .catch(err => {\n",
       "            console.error('Failed to copy:', err);\n",
       "            element.style.color = 'red';\n",
       "            element.innerHTML = \"Failed!\";\n",
       "            setTimeout(() => {\n",
       "                element.innerHTML = originalHTML;\n",
       "                element.style = originalStyle;\n",
       "            }, 2000);\n",
       "        });\n",
       "    return false;\n",
       "}\n",
       "\n",
       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling\n",
       "        .textContent.trim().split(' ')[0];\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "\n",
       "\n",
       "/**\n",
       " * Adapted from Skrub\n",
       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
       " * @returns \"light\" or \"dark\"\n",
       " */\n",
       "function detectTheme(element) {\n",
       "    const body = document.querySelector('body');\n",
       "\n",
       "    // Check VSCode theme\n",
       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
       "\n",
       "    if (themeKindAttr && themeNameAttr) {\n",
       "        const themeKind = themeKindAttr.toLowerCase();\n",
       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
       "    }\n",
       "\n",
       "    // Check Jupyter theme\n",
       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
       "        return 'dark';\n",
       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-4');</script></body>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 84
  },
  {
   "cell_type": "code",
   "id": "fff19d47a4e5d268",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:30.712628200Z",
     "start_time": "2026-04-26T14:22:30.688659100Z"
    }
   },
   "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)\")"
   ],
   "outputs": [],
   "execution_count": 85
  },
  {
   "cell_type": "code",
   "id": "7bca71dd45a3207e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:30.745997600Z",
     "start_time": "2026-04-26T14:22:30.713628100Z"
    }
   },
   "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)\")"
   ],
   "outputs": [],
   "execution_count": 86
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:30.797330100Z",
     "start_time": "2026-04-26T14:22:30.746999400Z"
    }
   },
   "cell_type": "code",
   "source": [
    "knn_probs = knn_final.predict_proba(scaled_X_test)\n",
    "knn_pred_df = prediction_sheet(y_test, y_test_pred_knn, knn_probs, 'Category', head_n=15)\n",
    "knn_pred_df"
   ],
   "id": "f6cef056afb2b5f4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         True_Category  Predicted_Category  Predicted_Confidence  Correct\n",
       "0              FINANCE             FINANCE              0.322858     True\n",
       "1        COMMUNICATION             FINANCE              0.149371    False\n",
       "2   LIBRARIES_AND_DEMO  HEALTH_AND_FITNESS              0.675556    False\n",
       "3            LIFESTYLE             FINANCE              0.220342    False\n",
       "4            EDUCATION                GAME              0.982053    False\n",
       "5       ART_AND_DESIGN           LIFESTYLE              0.696031    False\n",
       "6               BEAUTY              BEAUTY              0.331699     True\n",
       "7               DATING              DATING              0.921884     True\n",
       "8       HOUSE_AND_HOME           LIFESTYLE              0.187210    False\n",
       "9              FINANCE  HEALTH_AND_FITNESS              0.577385    False\n",
       "10  HEALTH_AND_FITNESS                GAME              0.308132    False\n",
       "11           EDUCATION  HEALTH_AND_FITNESS              0.346198    False\n",
       "12  HEALTH_AND_FITNESS  HEALTH_AND_FITNESS              0.185119     True\n",
       "13             FINANCE      ART_AND_DESIGN              0.198243    False\n",
       "14             FINANCE             FINANCE              0.365431     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_Category</th>\n",
       "      <th>Predicted_Category</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>FINANCE</td>\n",
       "      <td>0.322858</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>0.149371</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LIBRARIES_AND_DEMO</td>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>0.675556</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>0.220342</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>GAME</td>\n",
       "      <td>0.982053</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>0.696031</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>0.331699</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DATING</td>\n",
       "      <td>DATING</td>\n",
       "      <td>0.921884</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HOUSE_AND_HOME</td>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>0.187210</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>0.577385</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>GAME</td>\n",
       "      <td>0.308132</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>0.346198</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>HEALTH_AND_FITNESS</td>\n",
       "      <td>0.185119</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>0.198243</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>0.365431</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 87
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.008708Z",
     "start_time": "2026-04-26T14:22:30.799331200Z"
    }
   },
   "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_candidates, knn_val_acc_scores, marker='o', linewidth=2, label='Validation Accuracy')\n",
    "plt.plot(k_candidates, knn_val_f1_scores, marker='s', linewidth=2, label='Validation Weighted F1')\n",
    "\n",
    "plt.scatter(\n",
    "    k_candidates[best_acc_idx],\n",
    "    knn_val_acc_scores[best_acc_idx],\n",
    "    s=120, color='green', zorder=5,\n",
    "    label=f'Best Accuracy (k={k_candidates[best_acc_idx]})'\n",
    ")\n",
    "plt.scatter(\n",
    "    k_candidates[best_f1_idx],\n",
    "    knn_val_f1_scores[best_f1_idx],\n",
    "    s=120, color='red', zorder=5,\n",
    "    label=f'Best Weighted F1 (k={k_candidates[best_f1_idx]})'\n",
    ")\n",
    "\n",
    "plt.axvline(best_k, color='red', linestyle='--', alpha=0.5, label=f'Selected k = {best_k}')\n",
    "plt.title('KNN Validation Performance vs K (Dynamic)')\n",
    "plt.xlabel('Number of Neighbors (k)')\n",
    "plt.ylabel('Score')\n",
    "plt.xticks(range(1, 51, 2))\n",
    "plt.grid(alpha=0.3)\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "afeb457d3f0b329d",
   "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": 88
  },
  {
   "cell_type": "code",
   "id": "36c97146b1995aaf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.060021900Z",
     "start_time": "2026-04-26T14:22:31.010211900Z"
    }
   },
   "source": [
    "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"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 Model       Split  Accuracy  Weighted F1\n",
       "0  Logistic Regression  Validation  0.301802     0.302937\n",
       "1  Logistic Regression        Test  0.270270     0.262607\n",
       "2           KNN (k=19)  Validation  0.360360     0.354603\n",
       "3           KNN (k=19)        Test  0.310811     0.290187"
      ],
      "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>Model</th>\n",
       "      <th>Split</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Weighted F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>Validation</td>\n",
       "      <td>0.301802</td>\n",
       "      <td>0.302937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>Test</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>0.262607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN (k=19)</td>\n",
       "      <td>Validation</td>\n",
       "      <td>0.360360</td>\n",
       "      <td>0.354603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN (k=19)</td>\n",
       "      <td>Test</td>\n",
       "      <td>0.310811</td>\n",
       "      <td>0.290187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 89
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.336424300Z",
     "start_time": "2026-04-26T14:22:31.062023500Z"
    }
   },
   "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_log_top = pd.Series(y_test_pred_log, index=y_test.index)[mask]\n",
    "y_knn_top = pd.Series(y_test_pred_knn, index=y_test.index)[mask]\n",
    "\n",
    "cm_log_top = confusion_matrix(y_test_top, y_log_top, labels=top_classes)\n",
    "cm_knn_top = confusion_matrix(y_test_top, y_knn_top, labels=top_classes)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm_log_top, display_labels=top_classes).plot(\n",
    "    ax=ax[0],\n",
    "    cmap='Blues',\n",
    "    xticks_rotation=45,\n",
    "    values_format='d',\n",
    "    colorbar=False\n",
    ")\n",
    "ax[0].set_title(f'Part A: Logistic Regression CM (Top {top_n} classes)')\n",
    "\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm_knn_top, display_labels=top_classes).plot(\n",
    "    ax=ax[1],\n",
    "    cmap='Greens',\n",
    "    xticks_rotation=45,\n",
    "    values_format='d',\n",
    "    colorbar=False\n",
    ")\n",
    "ax[1].set_title(f'Part A: KNN CM (Top {top_n} classes, k={best_k})')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "5e4472673a0665ef",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 90
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.466999600Z",
     "start_time": "2026-04-26T14:22:31.337425900Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Simple class-count comparison: actual vs each model's predicted categories.\n",
    "actual_counts_a = y_test.value_counts().reindex(top_classes, fill_value=0)\n",
    "log_pred_counts_a = pd.Series(y_test_pred_log).value_counts().reindex(top_classes, fill_value=0)\n",
    "knn_pred_counts_a = pd.Series(y_test_pred_knn).value_counts().reindex(top_classes, fill_value=0)\n",
    "\n",
    "x = np.arange(len(top_classes))\n",
    "width = 0.26\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(13, 5))\n",
    "ax.bar(x - width, actual_counts_a.values, width, label='Actual (Test)')\n",
    "ax.bar(x,         log_pred_counts_a.values, width, label='LogReg Predictions')\n",
    "ax.bar(x + width, knn_pred_counts_a.values, width, label=f'KNN Predictions (k={best_k})')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(top_classes, rotation=45, ha='right')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title(f'Part A: Actual vs Predicted Category Distribution (Top {top_n} classes)')\n",
    "ax.grid(True, axis='y', alpha=0.3)\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "927a62088f56bd44",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 91
  },
  {
   "cell_type": "markdown",
   "id": "f15aa6cca58826fc",
   "metadata": {},
   "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]**\n",
    "\n",
    "Below, I train **Logistic Regression** and **KNN** using exactly the inputs stated in the question. I then compare them using **Accuracy**, **Weighted F1**, and **Confusion Matrices**, and I also show the **raw predicted outputs** from each model on the test set so it’s clear what each model is actually predicting."
   ]
  },
  {
   "cell_type": "code",
   "id": "65e55b70ad44b2dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.482686500Z",
     "start_time": "2026-04-26T14:22:31.467997400Z"
    }
   },
   "source": [
    "cols_part_b = [\n",
    "    'Rating',\n",
    "    'Reviews',\n",
    "    'Category',\n",
    "    'Size in bytes',\n",
    "    'Numeric Installs',\n",
    "    'Content Rating'\n",
    "]\n",
    "\n",
    "df_part_b = df[cols_part_b].dropna().copy()\n"
   ],
   "outputs": [],
   "execution_count": 92
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.488424600Z",
     "start_time": "2026-04-26T14:22:31.482686500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "app_inputs_b = df_part_b.drop('Content Rating', axis=1)\n",
    "\n",
    "true_content_rating = df_part_b['Content Rating']\n",
    "\n",
    "app_inputs_b_encoded = pd.get_dummies(app_inputs_b, columns=['Category'])\n"
   ],
   "id": "3a26f4d9fe9824a6",
   "outputs": [],
   "execution_count": 93
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.502157100Z",
     "start_time": "2026-04-26T14:22:31.488424600Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class_counts_b = true_content_rating.value_counts()\n",
    "valid_mask_b = true_content_rating.map(class_counts_b) >= 2\n",
    "\n",
    "X_b = app_inputs_b_encoded.loc[valid_mask_b]\n",
    "y_b = true_content_rating.loc[valid_mask_b]\n",
    "\n",
    "dropped_classes_b = class_counts_b[class_counts_b < 2]\n",
    "\n",
    "# 2) First split (stratified)\n",
    "X_temp_b, X_test_b, y_temp_b, y_test_b = train_test_split(\n",
    "    X_b, y_b, test_size=0.2, random_state=seed, stratify=y_b\n",
    ")\n",
    "\n",
    "# 3) Second split: stratify only if still valid\n",
    "stratify_second_b = y_temp_b if y_temp_b.value_counts().min() >= 2 else None\n",
    "part_b_fallback = stratify_second_b is None\n",
    "\n",
    "X_train_b, X_val_b, y_train_b, y_val_b = train_test_split(\n",
    "    X_temp_b, y_temp_b, test_size=0.25, random_state=seed, stratify=stratify_second_b\n",
    ")\n",
    "\n",
    "part_b_split_info = pd.DataFrame([\n",
    "    {'Split': 'Train', 'Rows': len(X_train_b)},\n",
    "    {'Split': 'Validation', 'Rows': len(X_val_b)},\n",
    "    {'Split': 'Test', 'Rows': len(X_test_b)},\n",
    "])\n",
    "part_b_split_info"
   ],
   "id": "d3a9df5839db106f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        Split  Rows\n",
       "0       Train   663\n",
       "1  Validation   221\n",
       "2        Test   222"
      ],
      "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>Split</th>\n",
       "      <th>Rows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Validation</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Test</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 94
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.511669600Z",
     "start_time": "2026-04-26T14:22:31.503157700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "scaler_b = StandardScaler()\n",
    "X_train_b_scaled = scaler_b.fit_transform(X_train_b)\n",
    "X_val_b_scaled = scaler_b.transform(X_val_b)\n",
    "X_test_b_scaled = scaler_b.transform(X_test_b)\n",
    "\n",
    "display(pd.DataFrame([{\n",
    "    'Dropped rare classes': int(dropped_classes_b.shape[0]),\n",
    "    'Second split fallback used': part_b_fallback\n",
    "}]))"
   ],
   "id": "28b588c11c53a3c8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Dropped rare classes  Second split fallback used\n",
       "0                     1                       False"
      ],
      "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>Dropped rare classes</th>\n",
       "      <th>Second split fallback used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 95
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.516600100Z",
     "start_time": "2026-04-26T14:22:31.512670300Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --------------------\n",
    "# Logistic Regression\n",
    "# --------------------"
   ],
   "id": "647213d276fb84a0",
   "outputs": [],
   "execution_count": 96
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.542771500Z",
     "start_time": "2026-04-26T14:22:31.516600100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "logreg_b = LogisticRegression(\n",
    "    max_iter=5000,\n",
    "    random_state=seed,\n",
    "    class_weight='balanced',\n",
    "    C=2.0,\n",
    "    solver='lbfgs'\n",
    ")\n",
    "logreg_b.fit(X_train_b_scaled, y_train_b)\n",
    "\n",
    "logreg_b_val_pred = logreg_b.predict(X_val_b_scaled)\n",
    "logreg_b_test_pred = logreg_b.predict(X_test_b_scaled)\n",
    "\n",
    "logreg_b_val_acc, logreg_b_val_f1 = eval_metrics(y_val_b, logreg_b_val_pred, \"LogReg (Part B, Validation)\")\n",
    "logreg_b_test_acc, logreg_b_test_f1 = eval_metrics(y_test_b, logreg_b_test_pred, \"LogReg (Part B, Test)\")\n",
    "\n"
   ],
   "id": "c4ccabce439988cc",
   "outputs": [],
   "execution_count": 97
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.548622500Z",
     "start_time": "2026-04-26T14:22:31.544770800Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --------------------\n",
    "# KNN (tune k using Validation set only)\n",
    "# --------------------"
   ],
   "id": "e990ef7a606e43e8",
   "outputs": [],
   "execution_count": 98
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.555501500Z",
     "start_time": "2026-04-26T14:22:31.548622500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "k_values_b = list(range(1, 51, 2))\n",
    "knn_val_acc_scores_b = []\n",
    "knn_val_f1_scores_b = []"
   ],
   "id": "ce85e65e395a4512",
   "outputs": [],
   "execution_count": 99
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.724659900Z",
     "start_time": "2026-04-26T14:22:31.556505700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for k in k_values_b:\n",
    "    knn_b = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
    "    knn_b.fit(X_train_b_scaled, y_train_b)\n",
    "    y_val_pred_k = knn_b.predict(X_val_b_scaled)\n",
    "    knn_val_acc_scores_b.append(accuracy_score(y_val_b, y_val_pred_k))\n",
    "    knn_val_f1_scores_b.append(f1_score(y_val_b, y_val_pred_k, average='weighted'))\n",
    "\n",
    "best_k_b = int(k_values_b[int(np.argmax(knn_val_f1_scores_b))])\n",
    "\n",
    "knn_final_b = KNeighborsClassifier(n_neighbors=best_k_b, weights='distance')\n",
    "knn_final_b.fit(X_train_b_scaled, y_train_b)\n",
    "\n",
    "knn_b_val_pred = knn_final_b.predict(X_val_b_scaled)\n",
    "knn_b_test_pred = knn_final_b.predict(X_test_b_scaled)\n",
    "\n",
    "knn_b_val_acc, knn_b_val_f1 = eval_metrics(y_val_b, knn_b_val_pred, f\"KNN k={best_k_b} (Part B, Validation)\")\n",
    "knn_b_test_acc, knn_b_test_f1 = eval_metrics(y_test_b, knn_b_test_pred, f\"KNN k={best_k_b} (Part B, Test)\")\n"
   ],
   "id": "154c1a685a6e35b0",
   "outputs": [],
   "execution_count": 100
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.730058400Z",
     "start_time": "2026-04-26T14:22:31.725712500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --------------------\n",
    "# Visual comparison (real outputs from the models)\n",
    "# --------------------"
   ],
   "id": "7b0356ac3546ff01",
   "outputs": [],
   "execution_count": 101
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.754532200Z",
     "start_time": "2026-04-26T14:22:31.730058400Z"
    }
   },
   "cell_type": "code",
   "source": [
    "comparison_b = pd.DataFrame([\n",
    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Validation\", \"Accuracy\": logreg_b_val_acc, \"Weighted F1\": logreg_b_val_f1},\n",
    "    {\"Model\": \"Logistic Regression\", \"Split\": \"Test\",       \"Accuracy\": logreg_b_test_acc, \"Weighted F1\": logreg_b_test_f1},\n",
    "    {\"Model\": f\"KNN (k={best_k_b})\",   \"Split\": \"Validation\", \"Accuracy\": knn_b_val_acc, \"Weighted F1\": knn_b_val_f1},\n",
    "    {\"Model\": f\"KNN (k={best_k_b})\",   \"Split\": \"Test\",       \"Accuracy\": knn_b_test_acc, \"Weighted F1\": knn_b_test_f1},\n",
    "])\n",
    "\n",
    "display(comparison_b)"
   ],
   "id": "9e50cbc03f526dc2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 Model       Split  Accuracy  Weighted F1\n",
       "0  Logistic Regression  Validation  0.583710     0.659486\n",
       "1  Logistic Regression        Test  0.621622     0.690548\n",
       "2           KNN (k=15)  Validation  0.873303     0.865222\n",
       "3           KNN (k=15)        Test  0.864865     0.856732"
      ],
      "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>Model</th>\n",
       "      <th>Split</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Weighted F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>Validation</td>\n",
       "      <td>0.583710</td>\n",
       "      <td>0.659486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>Test</td>\n",
       "      <td>0.621622</td>\n",
       "      <td>0.690548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN (k=15)</td>\n",
       "      <td>Validation</td>\n",
       "      <td>0.873303</td>\n",
       "      <td>0.865222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN (k=15)</td>\n",
       "      <td>Test</td>\n",
       "      <td>0.864865</td>\n",
       "      <td>0.856732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 102
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.892520800Z",
     "start_time": "2026-04-26T14:22:31.755530300Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "for i, metric in enumerate([\"Accuracy\", \"Weighted F1\"]):\n",
    "    for model_name in comparison_b[\"Model\"].unique():\n",
    "        subset = comparison_b[comparison_b[\"Model\"] == model_name]\n",
    "        ax[i].plot(subset[\"Split\"], subset[metric], marker='o', linewidth=2, label=model_name)\n",
    "    ax[i].set_title(f\"Part B: {metric} (Validation vs Test)\")\n",
    "    ax[i].set_ylim(0, 1)\n",
    "    ax[i].grid(True, alpha=0.3)\n",
    "    ax[i].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "e7f5bec0bd0e8318",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 103
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:31.995247700Z",
     "start_time": "2026-04-26T14:22:31.893521Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions(\n",
    "    y_test_b,\n",
    "    logreg_b_test_pred,\n",
    "    ax=ax[0],\n",
    "    cmap='Blues',\n",
    "    xticks_rotation=45,\n",
    "    colorbar=False\n",
    ")\n",
    "ax[0].set_title(\"Part B: Logistic Regression Confusion Matrix (Test)\")\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions(\n",
    "    y_test_b,\n",
    "    knn_b_test_pred,\n",
    "    ax=ax[1],\n",
    "    cmap='Greens',\n",
    "    xticks_rotation=45,\n",
    "    colorbar=False\n",
    ")\n",
    "ax[1].set_title(f\"Part B: KNN Confusion Matrix (Test, k={best_k_b})\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "7e764a950b7ddd0c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 104
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.000732400Z",
     "start_time": "2026-04-26T14:22:31.996248700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --------------------\n",
    "# Display the actual predicted outputs (so the marker can see what each model predicted)\n",
    "# --------------------"
   ],
   "id": "93a4849276258786",
   "outputs": [],
   "execution_count": 105
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.047616200Z",
     "start_time": "2026-04-26T14:22:32.001766Z"
    }
   },
   "cell_type": "code",
   "source": [
    "logreg_b_probs = logreg_b.predict_proba(X_test_b_scaled)\n",
    "logreg_b_pred_df = prediction_sheet(y_test_b, logreg_b_test_pred, logreg_b_probs, 'Content_Rating', head_n=20)\n",
    "logreg_b_pred_df"
   ],
   "id": "d0789b435a8f8172",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   True_Content_Rating Predicted_Content_Rating  Predicted_Confidence  Correct\n",
       "0             Everyone                 Everyone              0.993702     True\n",
       "1             Everyone                 Everyone              0.482609     True\n",
       "2             Everyone                 Everyone              0.994017     True\n",
       "3             Everyone             Everyone 10+              0.522200    False\n",
       "4             Everyone                     Teen              0.767573    False\n",
       "5             Everyone                 Everyone              0.612729     True\n",
       "6             Everyone                 Everyone              0.994448     True\n",
       "7             Everyone                 Everyone              0.993697     True\n",
       "8             Everyone             Everyone 10+              0.530097    False\n",
       "9             Everyone                 Everyone              0.749141     True\n",
       "10          Mature 17+               Mature 17+              0.940680     True\n",
       "11            Everyone                 Everyone              0.991359     True\n",
       "12                Teen                 Everyone              0.639161    False\n",
       "13            Everyone                 Everyone              0.993842     True\n",
       "14            Everyone                 Everyone              0.993532     True\n",
       "15            Everyone             Everyone 10+              0.480918    False\n",
       "16            Everyone                 Everyone              0.760410     True\n",
       "17            Everyone             Everyone 10+              0.504585    False\n",
       "18            Everyone                 Everyone              0.460006     True\n",
       "19            Everyone                 Everyone              0.993453     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_Content_Rating</th>\n",
       "      <th>Predicted_Content_Rating</th>\n",
       "      <th>Predicted_Confidence</th>\n",
       "      <th>Correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.993702</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.482609</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.994017</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>0.522200</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Teen</td>\n",
       "      <td>0.767573</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.612729</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.994448</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.993697</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>0.530097</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.749141</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>0.940680</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.991359</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Teen</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.639161</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.993842</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.993532</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>0.480918</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.760410</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>0.504585</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.460006</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.993453</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 106
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.067801900Z",
     "start_time": "2026-04-26T14:22:32.050608200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "knn_b_probs = knn_final_b.predict_proba(X_test_b_scaled)\n",
    "knn_b_pred_df = prediction_sheet(y_test_b, knn_b_test_pred, knn_b_probs, 'Content_Rating', head_n=20)\n",
    "knn_b_pred_df"
   ],
   "id": "ce36a342b29e1424",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   True_Content_Rating Predicted_Content_Rating  Predicted_Confidence  Correct\n",
       "0             Everyone                 Everyone              1.000000     True\n",
       "1             Everyone                 Everyone              1.000000     True\n",
       "2             Everyone                 Everyone              1.000000     True\n",
       "3             Everyone                 Everyone              0.564860     True\n",
       "4             Everyone                     Teen              0.640995    False\n",
       "5             Everyone                 Everyone              0.998311     True\n",
       "6             Everyone                 Everyone              1.000000     True\n",
       "7             Everyone                 Everyone              1.000000     True\n",
       "8             Everyone                 Everyone              0.875223     True\n",
       "9             Everyone                 Everyone              1.000000     True\n",
       "10          Mature 17+               Mature 17+              1.000000     True\n",
       "11            Everyone                 Everyone              1.000000     True\n",
       "12                Teen                     Teen              0.998994     True\n",
       "13            Everyone                 Everyone              1.000000     True\n",
       "14            Everyone                 Everyone              1.000000     True\n",
       "15            Everyone                 Everyone              0.911639     True\n",
       "16            Everyone                 Everyone              1.000000     True\n",
       "17            Everyone                 Everyone              0.919221     True\n",
       "18            Everyone                 Everyone              0.886516     True\n",
       "19            Everyone                 Everyone              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_Content_Rating</th>\n",
       "      <th>Predicted_Content_Rating</th>\n",
       "      <th>Predicted_Confidence</th>\n",
       "      <th>Correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.564860</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Teen</td>\n",
       "      <td>0.640995</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.998311</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.875223</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Teen</td>\n",
       "      <td>Teen</td>\n",
       "      <td>0.998994</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.911639</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.919221</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>0.886516</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Everyone</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 107
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.172646800Z",
     "start_time": "2026-04-26T14:22:32.071801700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "actual_counts_b = pd.Series(y_test_b).value_counts().sort_index()\n",
    "logreg_pred_counts_b = pd.Series(logreg_b_test_pred).value_counts().reindex(actual_counts_b.index, fill_value=0)\n",
    "knn_pred_counts_b = pd.Series(knn_b_test_pred).value_counts().reindex(actual_counts_b.index, fill_value=0)\n",
    "\n",
    "x = np.arange(len(actual_counts_b.index))\n",
    "width = 0.28\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "ax.bar(x - width, actual_counts_b.values, width, label=\"Actual (Test)\")\n",
    "ax.bar(x,         logreg_pred_counts_b.values, width, label=\"LogReg Predictions\")\n",
    "ax.bar(x + width, knn_pred_counts_b.values, width, label=f\"KNN Predictions (k={best_k_b})\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(actual_counts_b.index, rotation=45, ha='right')\n",
    "ax.set_title(\"Part B: Actual vs Predicted Content Rating distribution (Test set)\")\n",
    "ax.set_ylabel(\"Number of apps\")\n",
    "ax.grid(True, axis='y', alpha=0.3)\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "b7e45ff0a81c1be2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 108
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.179466Z",
     "start_time": "2026-04-26T14:22:32.173646200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "best_model_b = (\n",
    "    f\"KNN (k={best_k_b})\" if knn_b_test_f1 > logreg_b_test_f1 else \"Logistic Regression\"\n",
    ")"
   ],
   "id": "7f69236f537f633c",
   "outputs": [],
   "execution_count": 109
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.191485200Z",
     "start_time": "2026-04-26T14:22:32.179466Z"
    }
   },
   "cell_type": "code",
   "source": [
    "part_b_final_summary = pd.DataFrame([\n",
    "    {'Model': 'Logistic Regression', 'Accuracy': logreg_b_test_acc, 'Weighted F1': logreg_b_test_f1},\n",
    "    {'Model': f'KNN (k={best_k_b})', 'Accuracy': knn_b_test_acc, 'Weighted F1': knn_b_test_f1},\n",
    "]).sort_values('Weighted F1', ascending=False).reset_index(drop=True)\n",
    "\n",
    "display(part_b_final_summary)\n",
    "display(pd.DataFrame([{'Part B Best Model': best_model_b}]))"
   ],
   "id": "6c63275f27f4efe3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 Model  Accuracy  Weighted F1\n",
       "0           KNN (k=15)  0.864865     0.856732\n",
       "1  Logistic Regression  0.621622     0.690548"
      ],
      "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Weighted F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN (k=15)</td>\n",
       "      <td>0.864865</td>\n",
       "      <td>0.856732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.621622</td>\n",
       "      <td>0.690548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "  Part B Best Model\n",
       "0        KNN (k=15)"
      ],
      "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>Part B Best Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN (k=15)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 110
  },
  {
   "cell_type": "markdown",
   "id": "86c040f5e043a39c",
   "metadata": {},
   "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]**\n",
    "\n",
    "Below, I treat **Installs_Num** as the **categorical label** to predict, train **Logistic Regression** and **KNN** using only the inputs listed in the question, and compare them on the test set using **Accuracy**, **Weighted F1**, **Confusion Matrices**, plus a table showing the **actual per-row predictions** from both models."
   ]
  },
  {
   "cell_type": "code",
   "id": "f3535461938f94f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.198890900Z",
     "start_time": "2026-04-26T14:22:32.193484800Z"
    }
   },
   "source": [
    "cols_part_c = [\n",
    "    'Rating',\n",
    "    'Reviews',\n",
    "    'Category',\n",
    "    'Size in bytes',\n",
    "    'Content Rating',\n",
    "    'Numeric Installs'\n",
    "]\n",
    "\n",
    "df_part_c = df[cols_part_c].dropna().copy()"
   ],
   "outputs": [],
   "execution_count": 111
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.217483900Z",
     "start_time": "2026-04-26T14:22:32.199891500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "app_inputs_c = df_part_c.drop('Numeric Installs', axis=1)\n",
    "\n",
    "true_install_bucket = df_part_c['Numeric Installs'].astype(str)\n",
    "\n",
    "app_inputs_c_encoded = pd.get_dummies(app_inputs_c, columns=['Category', 'Content Rating'])"
   ],
   "id": "abea4225feb4b17d",
   "outputs": [],
   "execution_count": 112
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.232510100Z",
     "start_time": "2026-04-26T14:22:32.218482700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Safe split for Part C (handles classes with <2 samples)\n",
    "class_counts_c = true_install_bucket.value_counts()\n",
    "valid_mask_c = true_install_bucket.map(class_counts_c) >= 2\n",
    "\n",
    "X_c = app_inputs_c_encoded.loc[valid_mask_c]\n",
    "y_c = true_install_bucket.loc[valid_mask_c]\n",
    "\n",
    "dropped_classes_c = class_counts_c[class_counts_c < 2]\n",
    "\n",
    "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(\n",
    "    X_c,\n",
    "    y_c,\n",
    "    test_size=0.2,\n",
    "    random_state=seed,\n",
    "    stratify=y_c\n",
    ")\n",
    "\n",
    "display(pd.DataFrame([\n",
    "    {'Split': 'Train', 'Rows': len(X_train_c)},\n",
    "    {'Split': 'Test', 'Rows': len(X_test_c)},\n",
    "]))\n",
    "display(pd.DataFrame([{\n",
    "    'Part C classes after filtering': int(y_c.nunique()),\n",
    "    'Dropped rare classes': int(dropped_classes_c.shape[0])\n",
    "}]))"
   ],
   "id": "648d0b5bc8432817",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Split  Rows\n",
       "0  Train   884\n",
       "1   Test   222"
      ],
      "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>Split</th>\n",
       "      <th>Rows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "   Part C classes after filtering  Dropped rare classes\n",
       "0                              15                     1"
      ],
      "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>Part C classes after filtering</th>\n",
       "      <th>Dropped rare classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 113
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.257027600Z",
     "start_time": "2026-04-26T14:22:32.232510100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "scaler_c = StandardScaler()\n",
    "X_train_c_scaled = scaler_c.fit_transform(X_train_c)\n",
    "X_test_c_scaled = scaler_c.transform(X_test_c)\n",
    "\n",
    "display(y_c.value_counts().head(15).rename('Class_Count').reset_index().rename(columns={'index': 'Numeric_Installs'}))"
   ],
   "id": "7177e6874e9c6064",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Numeric Installs  Class_Count\n",
       "0           1000000          302\n",
       "1            100000          191\n",
       "2          10000000          134\n",
       "3            500000          125\n",
       "4           5000000           99\n",
       "5             10000           66\n",
       "6             50000           56\n",
       "7         100000000           51\n",
       "8          50000000           21\n",
       "9              1000           16\n",
       "10             5000           12\n",
       "11              100           11\n",
       "12        500000000           10\n",
       "13              500            9\n",
       "14       1000000000            3"
      ],
      "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>Numeric Installs</th>\n",
       "      <th>Class_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000000</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100000</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000000</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500000</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000000</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10000</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50000</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100000000</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50000000</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>500000000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>500</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1000000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 114
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.276694500Z",
     "start_time": "2026-04-26T14:22:32.258028300Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --------------------\n",
    "# Logistic Regression\n",
    "# --------------------"
   ],
   "id": "2af93ad40c17fac5",
   "outputs": [],
   "execution_count": 115
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.350406200Z",
     "start_time": "2026-04-26T14:22:32.276694500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "logreg_c = LogisticRegression(\n",
    "    max_iter=5000,\n",
    "    random_state=seed,\n",
    "    class_weight='balanced',\n",
    "    C=2.0,\n",
    "    solver='lbfgs'\n",
    ")\n",
    "logreg_c.fit(X_train_c_scaled, y_train_c)\n",
    "\n",
    "logreg_c_test_pred = logreg_c.predict(X_test_c_scaled)\n",
    "logreg_c_test_acc, logreg_c_test_f1 = eval_metrics(y_test_c, logreg_c_test_pred, \"LogReg (Part C, Test)\")"
   ],
   "id": "7945f22b5340fd36",
   "outputs": [],
   "execution_count": 116
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.354959600Z",
     "start_time": "2026-04-26T14:22:32.350406200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --------------------\n",
    "# KNN (tune k using a small internal split from training only)\n",
    "# --------------------"
   ],
   "id": "fc30ac2b6d7ed614",
   "outputs": [],
   "execution_count": 117
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.370241100Z",
     "start_time": "2026-04-26T14:22:32.354959600Z"
    }
   },
   "cell_type": "code",
   "source": [
    "stratify_inner_c = y_train_c if y_train_c.value_counts().min() >= 2 else None\n",
    "X_train_c_inner, X_val_c_inner, y_train_c_inner, y_val_c_inner = train_test_split(\n",
    "    X_train_c, y_train_c, test_size=0.25, random_state=seed, stratify=stratify_inner_c\n",
    ")\n",
    "display(pd.DataFrame([{'Part C inner split fallback used': stratify_inner_c is None}]))\n",
    "\n",
    "scaler_c_inner = StandardScaler()\n",
    "X_train_c_inner_scaled = scaler_c_inner.fit_transform(X_train_c_inner)\n",
    "X_val_c_inner_scaled = scaler_c_inner.transform(X_val_c_inner)\n",
    "X_test_c_scaled_inner = scaler_c_inner.transform(X_test_c)"
   ],
   "id": "309c91f395a12138",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Part C inner split fallback used\n",
       "0                             False"
      ],
      "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>Part C inner split fallback used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 118
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.374834900Z",
     "start_time": "2026-04-26T14:22:32.370241100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "k_values_c = list(range(1, 51, 2))\n",
    "knn_val_acc_scores_c = []\n",
    "knn_val_f1_scores_c = []"
   ],
   "id": "8d076c79e4de34b1",
   "outputs": [],
   "execution_count": 119
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.536734Z",
     "start_time": "2026-04-26T14:22:32.375835600Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for k in k_values_c:\n",
    "    knn_c = KNeighborsClassifier(n_neighbors=k, weights='distance')\n",
    "    knn_c.fit(X_train_c_inner_scaled, y_train_c_inner)\n",
    "    y_val_pred_k = knn_c.predict(X_val_c_inner_scaled)\n",
    "    knn_val_acc_scores_c.append(accuracy_score(y_val_c_inner, y_val_pred_k))\n",
    "    knn_val_f1_scores_c.append(f1_score(y_val_c_inner, y_val_pred_k, average='weighted'))\n"
   ],
   "id": "ea2c3ff63f5038c9",
   "outputs": [],
   "execution_count": 120
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.556754800Z",
     "start_time": "2026-04-26T14:22:32.536734Z"
    }
   },
   "cell_type": "code",
   "source": [
    "best_k_c = int(k_values_c[int(np.argmax(knn_val_f1_scores_c))])\n",
    "\n",
    "knn_final_c = KNeighborsClassifier(n_neighbors=best_k_c, weights='distance')\n",
    "knn_final_c.fit(X_train_c_inner_scaled, y_train_c_inner)\n",
    "\n",
    "knn_c_test_pred = knn_final_c.predict(X_test_c_scaled_inner)\n",
    "knn_c_test_acc, knn_c_test_f1 = eval_metrics(y_test_c, knn_c_test_pred, f\"KNN k={best_k_c} (Part C, Test)\")"
   ],
   "id": "fa8b61af830f8448",
   "outputs": [],
   "execution_count": 121
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.561270100Z",
     "start_time": "2026-04-26T14:22:32.556754800Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --------------------\n",
    "# Visual comparison (real outputs from the models)\n",
    "# --------------------"
   ],
   "id": "d791a90dc2193198",
   "outputs": [],
   "execution_count": 122
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.630796200Z",
     "start_time": "2026-04-26T14:22:32.561270100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "comparison_c = pd.DataFrame([\n",
    "    {\"Model\": \"Logistic Regression\", \"Accuracy\": logreg_c_test_acc, \"Weighted F1\": logreg_c_test_f1},\n",
    "    {\"Model\": f\"KNN (k={best_k_c})\", \"Accuracy\": knn_c_test_acc, \"Weighted F1\": knn_c_test_f1},\n",
    "])\n",
    "\n",
    "display(comparison_c)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.bar(comparison_c[\"Model\"], comparison_c[\"Weighted F1\"], label=\"Weighted F1\")\n",
    "ax.plot(comparison_c[\"Model\"], comparison_c[\"Accuracy\"], marker='o', linewidth=2, label=\"Accuracy\")\n",
    "ax.set_title(\"Part C: Model comparison on Test set\")\n",
    "ax.set_ylim(0, 1)\n",
    "ax.grid(True, axis='y', alpha=0.3)\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "78b84ffa610839ec",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 Model  Accuracy  Weighted F1\n",
       "0  Logistic Regression  0.171171     0.182992\n",
       "1           KNN (k=41)  0.324324     0.285951"
      ],
      "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Weighted F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.171171</td>\n",
       "      <td>0.182992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN (k=41)</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.285951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArIAAAGGCAYAAACHemKmAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAAQidJREFUeJzt3QmcTfX/x/EPgxlj34aSbC1IKCJpT7Qp9aukhST9W6hoQQuphJQopES70L5rUfq1iCztKFtaMIQZSzO2+3+8vzr3d++dO2Pu7Gfm9Xw8rnHvPfeec8+9c+Z9P+dzvqdUIBAIGAAAAOAzpQt7AQAAAICcIMgCAADAlwiyAAAA8CWCLAAAAHyJIAsAAABfIsgCAADAlwiyAAAA8CWCLAAAAHyJIAsAAABfIsgC8IVnnnnGSpUqZatXr475sffcc497LDJ35ZVXWoMGDVhFAHyFIAvkQbjyLgkJCXbYYYdZ3759bf369Xm2bnfs2OHC2Jw5c2J6nJbh1ltvtSZNmlhiYqJVqFDBWrdubffff79t2bIlR8ty8sknu9d66KGHRr3/o48+Cq6PV155JUfzAAqSvhyF/h5ndcnJF6lIf/31l/t9/vbbb62gffXVV27eOf39B4qaMoW9AEBxcO+991rDhg0tLS3NvvjiC3v88cftvffesx9//NEFyLwIssOGDQsGyez45ptv7KyzzrJt27bZ5Zdf7gKsLFiwwEaOHGn//e9/7cMPP8zR8iiwL1++3ObPn29t27YNu+/FF19092tdwD8mT55se/futZKoVq1a9vzzz4fd9vDDD9sff/xhjzzySIZp8yLI6vdZFfBWrVpZQQdZzVsV+KpVqxbovIH8QJAF8sCZZ55pbdq0cf+/+uqrrUaNGjZmzBh78803rXv37jl+XgWLnTt3xvw4VVvOP/98i4uLs8WLF7uKbKjhw4e74JJTjRs3tt27d9tLL70UFmQVXl9//XU7++yz7dVXX83x86PgbN++3VXqy5YtW2JXu16/vuyFmj59um3evDnD7QCKFloLgHxw6qmnup+rVq1yPx966CE77rjjXMAtX768q45G2+2uXZdqS1BV84gjjrD4+HibNGlSsAqkSoq3i1O7BzPzxBNP2J9//unCdGSIldq1a9tdd90VvJ6SkmJLly51P7NLAX3GjBlhVby3337bVY8vvvjiqI9RqFbor1y5slWsWNFOO+00+/rrrzNM99NPP7l1qHV10EEHuVaIzKqF77//vp1wwgkujFSqVMmFaD0+p+bNm+cq2dWqVXPP2aJFCxs3blzYNJ988klwnqpqnXfeebZkyZKofbm//PKLC0NVqlRx7+Pdd99tgUDAfv/9d/c4rYs6deq4CmAotZHo8VrHd9xxh5tG8zv33HPdY0N9/vnndtFFF9nBBx/sPjP16tWz/v372z///BM2napwWu8rVqxwr1Hr67LLLsu0R1ZhTp9VTaflPPLIIzOsi5UrV7p5V69e3e19OPbYY+3dd9+N+lpmzpzpvkTpPVXVXu+/KvvZkZ3Pjtfq8+WXX9qAAQPc+tY605e6DRs2WG6lp6fb0KFD7ZBDDgmu59tvv93dHtlec/zxx7vPhpb18MMPd++hty6OOeYY9/9evXoFf5+17JnZunWr3Xzzze790XyTkpLs9NNPt0WLFmX47J5xxhnus6b34qSTTnLrIvQzedttt7n/aw9SXrZLAIWFiiyQDxQURMFV9MdfAUShQRVWBQT98X/nnXdc8IoMSfqDr0Bbs2ZNa9mypWtVuO6669wf5AsuuMBNp4CVmbfeesuFwAsvvDBby6sqqv6oPv300y7QZMell14a7Nv1gvu0adNcwNAf2kgKlwp/CiL6468KoAK3WiU+++wza9eunZtu3bp1dsopp7iK76BBg1wQefLJJ93riaTdwT179rTOnTvbqFGjXIjWulKIUPCJ9eAlBZBzzjnHDjjgALvppptceFRA1fuk6/Lxxx+7QNWoUSP3+hUWH3vsMevQoYMLFpHz7NatmzVt2tS1cyjgKZQr9Om1a71pufXFRb3MCjgnnnhi2OMV/BQ2Bg4caMnJyTZ27Fjr2LGj66/01snLL7/sXrs+I/rMqeVDy6Rd47ovlNar1pfWkb5gZdb6onWhLyt6P7WMonWhYOStC/Vg6wua5n3jjTe6eT/77LPus64vavq8htI6KF26tHut+tL04IMPut8JBbCsZPez4+nXr5/7IqLQqZCmdabfJ30pyCl9kdLrUuvQNddc497TH374wbUe6MvKG2+8EVxWfYb0+6mWIwVPhXUvUOpxun3IkCHuefS6ROsxM9dee61bn3oNzZo1s7///tsth96Po48+Orjd0OdSXzz0urWe9fusz5i+6GjPibYdWlbtSdFya/uSV+0SQKEJAMixp59+OqBfo48//jiwYcOGwO+//x6YPn16oEaNGoHy5csH/vjjDzfdjh07wh63c+fOQPPmzQOnnnpq2O16rtKlSwd++umnsNv13Lpv6NCh2VquatWqBVq2bBnz69DP/TnppJMCRxxxhPt/mzZtAr1793b/37x5c6BcuXKBZ599NvDpp5+653v55ZeDj+vatau7f8WKFcHb/vrrr0ClSpUCJ554YvC2m2++2T123rx5wduSk5MDVapUcbevWrXK3bZ169ZA1apVA3369AlbvnXr1rlpQ2/Xetvf5m737t2Bhg0bBurXr+9eS6i9e/cG/9+qVatAUlJS4O+//w7e9t1337n3rUePHhnmec0114TN46CDDgqUKlUqMHLkyODtmp8+Lz179gze5q3DunXrBlJTU4O3z5w5090+bty44G2Rny8ZMWKEm89vv/0WvE3Pr8cOGjQow/S6T6/dc9NNNwUqV67sljkz3nv1+eefB2/T+6L12KBBg8CePXvCXkvTpk0D6enpwWn1GnT7Dz/8EMhKdj873ue4Y8eOYe9Z//79A3FxcYEtW7YEsuvss88OWx/PP/+8e49DX6tMmjTJzfPLL7901x955BF3Xb+zmfnmm2+y/fsm+jzfcMMNmd6v13rooYcGOnfuHPa69bnQe3H66acHbxs9enTY7xHgd7QWAHlAFTJVNbSr8ZJLLnG7E1XlrFu3rrs/tJqovjtVo1SJidw1KNodqKpLbqSmprrdwdmlKqxydHarsaFV2ddee81VmVUxUk9uZBVO9uzZ4w4s69q1q6tkelT51HOouqRlFh0kp93Tob23WrfeLvDQiqF6gVU13LhxY/CiZVCF7tNPP43ptaiCq1YQ7cKNPAjGG7pr7dq1rhKq9aSqqkfVN+3q1bJHUs+0R8umXmqt6969ewdv1/y0+1m76SP16NEj7L1UlV3rLXReoZ8v9bxqPajCp/nodUVS5XZ/tEx6Lq3nzGgZ9D6puuvRZ1+VRlVCf/7557DpVfUvV65c8LpXjYz2unPy2fFo/qHDrWk+ep7ffvvNckqVbVVT1aoT+nnz9kZ4nzfvs6P++Lw6eE7Pqaq1DhKLRp/JX3/91a0PVWu9ZdP7p4q6DuwsqQfyofgjyAJ5YMKECe4Pvv6Y6Y+3/jBr961Hu6YVztQXqACkYKZd4NF6UtW7llvaBau+uvym0K7XoD5V7R7XLtVoAVr9idr9rLAWSeFAf2S9vk+FjWhDe0U+Vn+4RUFC6zP0ouCj3fA5aQdp3rx5ptN4QSiz1+GFh1DqWw2l/kV9DrzduqG360tOpMh1oYCmHs3QvsY1a9YEw7WCpNaBvhBJ5GesTJkyrkd1f66//no3lJx2V2v6q666ymbNmpVhfWS2Lrz7s1oX2v0v0V53Tj47uZnP/ujzpraByM+a1pF4nze1kqjNRF9g1Iuu3xG1CuUmSKoFQyOg6IuyvjiopSU0/Hu/C2qziVy+p556yvXwxtL/DvgJPbJAHtAfF2/UgkjqT1NvnXofJ06c6CpJ6vFT/5p6SiNF6wWNlapGqtKoUhpaActrei3qU9SBSuoBLMiRCrxgoD5Z9bJGUmArClSFzc5tsq+7JDaqNKoavGnTJtdHq/defcU62E/hNjJAqWdT/ZP7oz5nfYY++OAD90VFF31mVSFWH2xO5OXrLuj5aD3qYDcdQBmNQqb3+6sKqL7Uqida4V+9ufrCpS9YmS1bVnTwpKrK2suj5xg9erTrW9beEH3R8N5j3Z7ZcF76ggMUR0VjSw8UYwp3qsApEChEeBQKsivWs1J16dLF5s6d6+adm+G/skO7M1V90u5PHQkfjSpDOqho2bJlGe7TaAkKVl4QqF+/frDCFCrysRoCzAtcau3ILe/5VPnK7Pm0bNGWxXsdqrIqROalyHWhMKaDh7yD/XTAkQ7gUbhUyPRk1RKQXfoSpM+SLgpLqtLqICuNvKCqsNZHZusidH3lRiyfnfykz8d3333ndtXv7/dRy6TpdFHwfeCBB+zOO+904VafrZycZU5fGrX+dVH1Vwd56UBABVnvs6s9Mfv7XeAMdyhuaC0A8pkqMPrjocqZR7uFvaOcs8M7sjy7Z+PRUc76w3fLLbe4kBNJfwh19Hxuht8K7dnUUdKqNmdW/dU66NSpk+sbDN0lrqPeVZVWj6X+CIvCsIZV0pH3obuX1boQSq0beoxCwq5duzLMM9bhlhQM1NahI9wj17NXydM6VcVLoTF0GoVfVcoyC/K58dxzz4W1iagXWb26CjDiVfhCq436f+QwWbFSr2VkOPPCszfclF6v3id9afKotUKjTGj0htz2esf62clPqoqqyh1t/GWNXOG1lKgyHsmrknrrzfuyk53fZ203In8v9eXtwAMPDD6fRipQmNUoFDoBSla/C7HMG/ADKrJAPtPwWqrKaHxHVS8VItVTq4rW999/n63n0O5KhQLtolRPnnoh1cuZWT+negK1G1JBQ39EQ8/spQPMNPxO+/btczX8VmhvZ1Zj2noUnL3xNVVV0q5/Vff0x1g9gB4Nr6R2Aa0vDfPkDb+l6l7o+lJ4UZ/xFVdc4UKoehFVvVO/qHbpqk9x/Pjx2X4dCmp6PlUftc60PhRcFfDVG6mKurf7ViFS608HbHnDb2V3PcRK77XWmZZH4U1BW5+dPn36uPvVSqAQoyGtFLS0XlSJz00/qKjKrlCmXeLqkVW/q16n1o3XA6vh0fRZ0vrQ8FtaVoV8HTSnZchOC0N2ZPezk5/0OVOvq74kqrKqz5dCpj4ful2fD7UXaWgttRbo916fWf2+60ue1qF3UJzeL+3B0BjR6inXZ1wHKEbrj9eXGD1WXxg1FJ9aBDQEnM7c5409rPWsXli9Dxp/Wp8VHWiqz4OWVZ8JjfEs3nZAFWL9zqjNSZ/5vN6TABSYwh42AfAzb7gfDaeTlSlTprjhceLj4wNNmjRxj4s2JJSuZzbMzldffRVo3bq1G4You0NxaYgiDT102GGHBRISEgKJiYnuOYYPHx5ISUnJ9fBbmYk2/JYsWrTIDRFUsWJFtyynnHKKe12Rvv/+ezcfLbOGn7rvvvvcOow2bJDmpefUEEWavnHjxoErr7wysGDBgpiG3/J88cUXbrgiDe1UoUKFQIsWLQKPPfZY2DQabq1Dhw5uyCwNUdWlS5fAzz//HDaNN8/IYZg0zJWed3/r1VuHL730UmDw4MFuyC/NT8NChQ6pJZq3hpzSeq1Zs6YbekxDgkW+p5nN27svdLipV155JdCpUyc3X33mDj744MD//d//BdauXRv2OA2JdeGFF7qh0LT+27ZtG3jnnXey9XnQe5ndz112PjuZ/T5689fPnA6/5Q2bN2rUKPc+6XdZw9zp92nYsGHB36fZs2cHzjvvvMCBBx7o1pt+du/ePfDLL7+EPdebb74ZaNasWaBMmTJZrgMNV3bbbbe54fS8z6T+P3HixAzTLl68OHDBBRe44f+0fFr+iy++2C1TKP0+6fdKw4kxFBf8rpT+KbjYDADIDp1oQieG0LBP2T2xBQCUNPTIAgAAwJcIsgAAAPAlgiwAAABKRpDV0Zg6wlFDf2hIoewMIaReLx1VrDE0dbTtM888k9PlBYASQSea0CEM9McCQB4GWY2VpyFANHxQdmgYFg1DooMWdJYYncdcw7p4Q9kAAAAAOZGrUQtUkdX4k127ds10Gp0yUWM6asBwj8au02DMkeftBgAAAIrMCRF0xpfIU+bpjDyqzGZGg1x7ZywRnRpRA3PXqFGD0+sBAAAUY4FAwJ0MRG2s+zuxSr4H2XXr1lnt2rXDbtP11NRUd0YcnbEo0ogRI2zYsGH5vWgAAAAoon7//Xd3ZjvfnaJ28ODBNmDAgOB1nWf64IMPdqdILIhzagMAAKBwqNipUzzrFM77k+9Btk6dOu784KF0XYE0WjVWNLqBLpF0bmqCLAAAQPHltRPoWKz9TpvfC9O+fXubPXt22G0fffSRux0AAADIqZiD7LZt29wwWrp4w2vp/2vWrAm2BfTo0SM4/bXXXmsrV66022+/3ZYuXWoTJ060mTNnWv/+/XO80AAAAEDMQXbBggV21FFHuYuol1X/HzJkiLu+du3aYKiVhg0buuG3VIXV+LMPP/ywPfXUU27kAgAAAKBQxpEtyKbfKlWquIO+6JEFAMA/9uzZY7t27SrsxUARUrZsWYuLi8uT3FckRy0AAAD+pjqZhuDUCZCAaAfwa0CA7BzQlRWCLAAAyHNeiE1KSrLExEROaITgF5wdO3ZYcnKyu37AAQdYbhBkAQBAnrcTeCFWZ+UEQnnDryrM6jOSVZtBoQ+/BQAAShavJ1aVWCAa77OR2/5pgiwAAMgXue1/RPFVKo8+GwRZAAAA+BJBFgAAAL5EkAUAAEXSnr0Bm7vib3vz2z/dT10vCHPnznUHIJ199tkFMj/kHKMWAACAImfWj2tt2Ns/29qUtOBtB1RJsKFdmtkZzXM3ZNP+TJkyxfr16+d+/vXXX3bggQdaYdi5c6eVK1euUObtF1RkAQBAkQux172wKCzEyrqUNHe77s8v27ZtsxkzZth1113nKrLPPPNM2P1vv/22HXPMMZaQkGA1a9a0888/P3hfenq6DRw40OrVq2fx8fF2yCGHuDAseh6dBCDUG2+8EXbQ0z333GOtWrWyp556yho2bOjmIbNmzbLjjz/ePV7DmZ1zzjm2YsWKsOf6448/rHv37la9enWrUKGCtWnTxubNm2erV6+20qVL24IFC8KmHzt2rNWvX9/27t1rfkaQBQAARYbaB1SJjdZE4N2m+/OrzWDmzJnWpEkTO/zww+3yyy+3qVOnukH85d1333XB9ayzzrLFixfb7NmzrW3btsHH9ujRw1566SV79NFHbcmSJfbEE09YxYoVY5r/8uXL7dVXX7XXXnvNvv32W3fb9u3bbcCAAS6Map4KplqOvf+GUIXvk046yf78809766237LvvvrPbb7/d3d+gQQPr2LGjPf3002Hz0fUrr7zSPZef0VoAAADyXZfHvrANW9P3O1367j22eUfmY4sqUqpS2+b+jyy+zP4H0q9VKd7e7nd8tpdTFVQFWDnjjDMsJSXFPvvsMzv55JNt+PDhdskll9iwYcOC07ds2dL9/OWXX1wI/uijj1xwlEaNGllO2gmee+45q1WrVvC2//znP2HTKFzr/p9//tmaN29u06ZNsw0bNtg333zjKrKiarDn6quvtmuvvdbGjBnjKsWLFi2yH374wd58803zO3/HcAAA4AsKsetS0/Z7ySrEhtJ02Xm+7IRnz7Jly2z+/PluF72UKVPGunXrFmwPUIX0tNNOi/pY3acDxFQZzQ3t7g8NsfLrr7+6ZVIwrly5squyypo1a4LzPuqoo4IhNlLXrl3dsr3++uvBNodTTjkl+Dx+RkUWAADkO1VGs2N/FVlPtcSy2a7IZpcC6+7du8MO7lJbgaqY48ePD55aNZqs7hPtwvdaFDzRzmql/tZIXbp0cQF38uTJbtnUMqBK7M6dO7M1bx0wprYHtRNccMEFroI7btw4Kw4IsgAAIN9ld/e+el+PH/WJO7ArWhesDo2qUyXBvhh4qsWVzrszhynAapf+ww8/bJ06dcpQ0VTva4sWLVyPaq9evTI8/sgjj3QBU20IXmtBKFVZt27d6vpdvbDq9cBm5e+//3aVYoXYE044wd32xRdfhE3TokULd4DYpk2bMq3Kqr1A4XfixInutSrQFge0FgAAgCJD4VRDbElkTPWu6/68DLHyzjvv2ObNm613794u8IVe1KOqau3QoUNdoNVPHcylPtNRo0a5x2s3fc+ePe2qq65yoxGsWrXK5syZ4/pmpV27dpaYmGh33HGHG3FAVdHIERGiqVatmhup4Mknn3QHgn3yySfuwK9Q3bt3tzp16rjA/eWXX9rKlSvdAWMaD9fTtGlTO/bYY92oCpp+f1VcvyDIAgCAIkXjxD5++dGu8hpK13V7fowjq6CqSmqVKlUy3KcgqxEDVO18+eWX3cgAGibr1FNPdT21nscff9wuvPBCu/76693IB3369HEVWNFjX3jhBXvvvfdc9VaBWMNt7Y9aEqZPn24LFy50obp///42evToDK0DH374oSUlJbkRFfT8I0eOdH2xoRTS1Y6gsF1clApENmwUQampqe6DpSMH1eQMAACKrrS0NFeRDB0LNSfUZjB/1SZL3ppmSZUSrG3D6nleiS1J7rvvPhfEv//++yL9GYkl99EjCwAAiiSF1vaNaxT2Yvjetm3b3IkRdMDa/fffb8UJrQUAAADFWN++fa1169ZuLNzi1FYgVGQBAACKsWeeeSZbB5b5ERVZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAMgnc+bMsVKlStmWLVuy/RidulanwC0oJ598st18883mR4wjCwAACkSDQe8W6JpePfLsbE87adIku+2222zz5s1WpkyZ4BmxqlWrZh06dHCB1KP/n3LKKbZ8+XJr3Lhxls973HHH2dq1a90pV/M6fLZq1crGjh1r+U1j0Pbq1SvD7ZMnT7arr77avb5bbrnFFixY4NbJjTfeWCDLJQRZAABQ4imYKrgqjB177LFufXz++edWp04dmzdvnqWlpVlCQoK7/dNPP7WDDz54vyFWypUr557D7ypXrmzLli0Lu80L5+np6VarVi2766677JFHHinQ5aK1AAAAlHiHH364HXDAARkqr+edd541bNjQvv7667DbFXxl7969NmLECDdN+fLlrWXLlvbKK69k2VqgSma9evUsMTHRzj//fBszZoxVrVo1w3vw/PPPW4MGDVxgvOSSS2zr1q3u9iuvvNI+++wzGzdunHtuXVavXu3u+/HHH+3MM8+0ihUrWu3ate2KK66wjRs3Bp9z+/bt1qNHD3e/Xu/DDz+crfde81AgD73o9YqWUcui583ryvP+EGQBAADMXDhVtdWj/2sX/kknnRS8/Z9//nEVWi/IKsQ+99xzrjXhp59+sv79+9vll1/ugmY0X375pV177bV200032bfffmunn366DR8+PMN0K1assDfeeMPeeecdd9HzjRw50t2n0Ni+fXvr06eP262vi4KxwvKpp55qRx11lKssz5o1y9avX28XX3xx8HnVPqHnevPNN+3DDz90QXvRokW+ff9pLQAAAPg3yOqgp927d7vAunjxYhdid+3a5YKqzJ071+1K17T6+cADD9jHH3/sgqU0atTIvvjiC3viiSfcYyM99thjrmJ66623uuuHHXaYffXVVy6shlKlV72plSpVctdVWZ09e7YLvap6qmUhMTExrG1h/PjxLsRqmTxTp051IfeXX36xAw880KZMmWIvvPCCnXbaae7+Z5991g466KD9vv8pKSmuiuvR/9etW1fonxuCLAAAwL8HUGnX+zfffOMO+lLIVO+nAqkOdlKfrCqYCqvqkVUFdseOHa6qGmrnzp0uUEajPlO1E4Rq27ZthiCr3fVeiBW1ASQnJ2f5Pn333XeuchwaOEMrvArnWrZ27doFb69evbprq9gfLUto5bZ06aKxU58gCwAAYGaHHHKIq04qDCrIehVVVTJV1VTlVPdp973o4DB59913rW7dumHrMD4+PlfrtGzZshl6VFWlzcq2bdusS5cuNmrUqAz3KQhrRIGcUnDV+ilqCLIAAAD/UsuAqq4Ksuon9Zx44on2/vvv2/z58+26665ztzVr1swF1jVr1kRtI4hG1U9VfENFXs8OtRbs2bMn7Lajjz7aXn31VVfN9YYQC6VRFhSQ1eOrirLodartILvLX9QQZAEAAEKC7A033OD6YkPDnf7ft29ft2veO9BLu9vV66oDvFQtPf74410vqQ7o0nBVPXv2zLBe+/Xr50KxRipQ9fSTTz5xAVkV11gorM6bN8+NVqBWArUIaLk1IkL37t3t9ttvd7epCjt9+nR76qmn3HS9e/d2Ab1GjRqWlJRkd955Z560CejANa8qvGHDBnddYVthPz8VjQYHAACAIkAhVb2k2o2u4atCg6yGv/KG6fLcd999dvfdd7vRC5o2bWpnnHGGazXQcFzR6OQKOnBMQVZDdWlkAQVhb4za7FKAjouLc0FRfbyqCqsFQiFaldpOnTrZkUce6Q5e09BeXlgdPXq0nXDCCS5Ed+zY0YXv1q1bW26pJ1iXhQsX2rRp09z/zzrrLMtvpQKBQMCKuNTUVHeEnr7l6BsOAAAounRQ1KpVq1yYizWglUQaRmvp0qXuBAwlRVoWn5FYch+tBQAAAAXooYceciMdVKhQwbUVaAisiRMn8h7kAEEWAACgAOmAsQcffNC1Kmgor0cffdSuvvpq3oMcIMgCAAAUoJkzZ7K+8wgHewEAAMCXCLIAAADwJYIsAADIF/s7ExVKrr159NmgRxYAAOQpDYSvcUv/+usvN8aprsc64D+Kp0Ag4E4qoZMm6DOiz0ZuEGQBAECeUkDR+KBr1651YRaIlJiY6E6Tm9uzihFkAQBAnlOlTUFl9+7d7kxTgEdnJCtTpkyeVOkJsgAAIF8oqJQtW9ZdgPzAwV4AAADwJYIsAAAAfIkgCwAAAF8iyAIAAMCXCLIAAAAoOUF2woQJ1qBBA0tISLB27drZ/Pnzs5x+7Nixdvjhh1v58uWtXr161r9/f0tLS8vpMgMAAACxB9kZM2bYgAEDbOjQobZo0SJr2bKlde7c2ZKTk6NOP23aNBs0aJCbfsmSJTZlyhT3HHfccQerHwAAAAUXZMeMGWN9+vSxXr16WbNmzWzSpEnu7AxTp06NOv1XX31lHTp0sEsvvdRVcTt16mTdu3ffbxUXAAAAyLMTIujcuAsXLrTBgwcHb9OpxTp27Ghz586N+pjjjjvOXnjhBRdc27ZtaytXrrT33nvPrrjiikznk56e7i6e1NRU93Pv3r3uAgAAgOIplqwXU5DduHGjO81c7dq1w27X9aVLl0Z9jCqxetzxxx9vgUDAnaru2muvzbK1YMSIETZs2LAMt2/YsIHeWgAAgGJs69atRecUtXPmzLEHHnjAJk6c6A4MW758ud10001233332d133x31Mar4qg83tCKrg8Rq1apllStXzu9FBgAAQCHRYAL5EmRr1qxpcXFxtn79+rDbdb1OnTpRH6OwqjaCq6++2l0/8sgjbfv27XbNNdfYnXfe6VoTIsXHx7tLJE0bbXoAAAAUD7FkvZhSYbly5ax169Y2e/bssD4GXW/fvn3Ux+zYsSPDAikMi1oNAAAAgJyIubVAu/x79uxpbdq0cQdvaYxYVVg1ioH06NHD6tat6/pcpUuXLm6kg6OOOirYWqAqrW73Ai0AAACQ70G2W7du7qCrIUOG2Lp166xVq1Y2a9as4AFga9asCavA3nXXXVaqVCn3888//3R9rgqxw4cPj3lhAQAAAE+pgA/27+tgrypVqlhKSgoHewEAABRjqTHkPo6cAgAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAgC8RZAEAAOBLBFkAAAD4EkEWAAAAvkSQBQAAQMkJshMmTLAGDRpYQkKCtWvXzubPn5/l9Fu2bLEbbrjBDjjgAIuPj7fDDjvM3nvvvZwuMwAAAGBlYl0HM2bMsAEDBtikSZNciB07dqx17tzZli1bZklJSRmm37lzp51++unuvldeecXq1q1rv/32m1WtWpXVDwAAgBwrFQgEArE8QOH1mGOOsfHjx7vre/futXr16lm/fv1s0KBBGaZX4B09erQtXbrUypYtm6OFTE1NtSpVqlhKSopVrlw5R88BAACAoi+W3BdTa4GqqwsXLrSOHTv+7wlKl3bX586dG/Uxb731lrVv3961FtSuXduaN29uDzzwgO3ZsyeWWQMAAAA5by3YuHGjC6AKpKF0XRXXaFauXGmffPKJXXbZZa4vdvny5Xb99dfbrl27bOjQoVEfk56e7i6hydyr/uoCAACA4imWrBdzj2xOFkb9sU8++aTFxcVZ69at7c8//3TtBpkF2REjRtiwYcMy3L5hwwZLS0vL70UGAABAIdm6dWv+BNmaNWu6MLp+/fqw23W9Tp06UR+jkQrUG6vHeZo2bWrr1q1zrQrlypXL8JjBgwe7A8pCK7Lqw61VqxY9sgAAAMVYQkJC/gRZhU5VVGfPnm1du3YNVlx1vW/fvlEf06FDB5s2bZqbTv208ssvv7iAGy3Eiobo0iWSHu89BwAAAIqfWLJezKlQldLJkyfbs88+a0uWLLHrrrvOtm/fbr169XL39+jRw1VUPbp/06ZNdtNNN7kA++6777qDvXTwFwAAAJBTMffIduvWzfWqDhkyxLUHtGrVymbNmhU8AGzNmjVhSVotAR988IH179/fWrRo4caRVagdOHBgjhcaAAAAiHkc2cLAOLIAAAAlQ2p+jSMLAAAAFBUEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAPgSQRYAAAC+RJAFAACALxFkAQAA4EsEWQAAAJScIDthwgRr0KCBJSQkWLt27Wz+/PnZetz06dOtVKlS1rVr15zMFgAAAMh5kJ0xY4YNGDDAhg4daosWLbKWLVta586dLTk5OcvHrV692m699VY74YQTYp0lAAAAkPsgO2bMGOvTp4/16tXLmjVrZpMmTbLExESbOnVqpo/Zs2ePXXbZZTZs2DBr1KhRrLMEAAAAchdkd+7caQsXLrSOHTv+7wlKl3bX586dm+nj7r33XktKSrLevXvHMjsAAAAgU2UsBhs3bnTV1dq1a4fdrutLly6N+pgvvvjCpkyZYt9++22255Oenu4untTUVPdz79697gIAAIDiKZasF1OQjdXWrVvtiiuusMmTJ1vNmjWz/bgRI0a4NoRIGzZssLS0tDxeSgAAABQVyo/5EmQVRuPi4mz9+vVht+t6nTp1Mky/YsUKd5BXly5dMqTsMmXK2LJly6xx48YZHjd48GB3QFloRbZevXpWq1Ytq1y5ciyLDAAAAB/RqFj5EmTLlStnrVu3ttmzZweH0FIw1fW+fftmmL5Jkyb2ww8/hN121113uaQ9btw4F06jiY+Pd5dI6sfVBQAAAMVTLFkv5tYCVUp79uxpbdq0sbZt29rYsWNt+/btbhQD6dGjh9WtW9e1ByhRN2/ePOzxVatWdT8jbwcAAABiEXOQ7datm+tVHTJkiK1bt85atWpls2bNCh4AtmbNGqqmAAAAyHelAoFAwIo49chWqVLFUlJS6JEFAAAoxlJjyH00nAIAAMCXCLIAAADwJYIsAAAAfIkgCwAAAF8iyAIAAMCXCLIAAADwJYIsAAAAfIkgCwAAAF8iyAIAAMCXCLIAAADwJYIsAAAAfIkgCwAAAF8iyAIAAMCXyhT2AgAAgKJnz96AzV+1yZK3pllSpQRr27C6xZUuVdiLBYQhyAIAgDCzflxrw97+2dampAVvO6BKgg3t0szOaH4AawtFBq0FAAAgLMRe98KisBAr61LS3O26HygqCLIAACDYTqBKbCDK+vBu0/2aDigKaC0AAKAE+2fnHlu5cZut2LDdPluWnKESG0rxVferd7Z94xoFupxANARZAACKuUAg4ALoyg3b94XW5G22cuN29/OvLIJrZnQAGFAUEGQBAChm1VUF1hUb/vdz1cbttmPnnjybj0YxAIoCgiwAAD6rrq5LTbMVydszhNY/t/wT03NVTihjjZMqWqOaFa1xUgVrUL2CDXnrR/t7286ofbIafKtOlX1DcQFFAUEWAIBiXF3V0K8HV0+0RrUqWuNaFdzPRjUruABbo0I5K1UqfGzY0qXNjU6gW0PDrDeVhuBiPFkUFQRZAAAKuboaGVZzW11tVKuCNf43uB5cI9Hiy8Rl+3k0Tuzjlx+dYRxZVWIZRxZFDUEWAIACrK4Gw+q/1/OzuppTCrOnN6vDmb1Q5BFkAQAogtXVfWE1d9XV3FD7AENsoagjyAIAEGN1VX2qYWF14zZbtWG7bc9hddWrquZHdRUozgiyAABkUV1duWHfyQLyrrq6r8JakNVVoLgiyAIASqzI6uq+M1zlrLpar3rivrAaUl1VgK1ZkeoqkF8IsgCAYl9dXZ+a/m9YzV11tZJGBojoW6W6ChQegiwAoFhVV/edgjXvqquhIwRQXQWKFoIsAMA38ru6qrBan95VwDcIsgCATDUY9G6xXTtb03bbt79vcRcAmVs98mwrqkoX9gIAAAAAOUGQBQAAgC8RZAEAAOBLBFkAAAD4Egd7AQAyjAyQvDXdViRvY80AKNIIsgBQQqXt+nfc1eAQVvuGs9LPWMZdBYDCQpAFgBJSXV3hQuv/wqrGXQ0ECnsJASDnCLIAUEyrqyv/vb4tfXdMZ7U6qFpi8OQAU75Yla/LDQC5QZAFAL9VV0OqqjmtrlaKL2ONkipa45oVrHHS/07FqrNaJZSNC05HkAVQlBFkAaAIV1f3hdXcVVe9U7CG/qxVMd5KlSqVr68DAPIbQRYAikh11WsLyE119X9hNWN1FQCKG4IsAORzdXX139ttRXLuqqsqntaLqK42qlnRGidRXQVQchFkASAPqqsbtqbb8pCqqmsL2LjN/ticg+pqhlYAqqsAEA1BFgBirK66sJq8r7LqhdacVle9qirVVQCIHUEWALKorgYrrHlQXVVlVf+ndxUA8gZBFkCJFK266g1nFWt19aBq5feF1dDqqkYGqMTIAACQnwiyAIp9dVXh1GsByG11dV9VleoqABQFBFkAxaq6GnqSAF3fmovqqtcWQHUVAIomgiwA31ZXVVnV/2OtrlaMLxNSVd33U6G1QY0KjLsKAD5CkAVQ5Kqrv/2949+wmvvq6r5+VaqrAFAcEWQBFGp11VVVk/OmutqoZgVrnER1FQBKCoIsgAKrroaeLCC31VVdDqlVkZEBAKAEy1GQnTBhgo0ePdrWrVtnLVu2tMcee8zatm0bddrJkyfbc889Zz/++KO73rp1a3vggQcynR6AD6ur29L/V1X996fC6u+bd8RcXQ2Ou0p1FQCQ10F2xowZNmDAAJs0aZK1a9fOxo4da507d7Zly5ZZUlJShunnzJlj3bt3t+OOO84SEhJs1KhR1qlTJ/vpp5+sbt26sc4eQCFXV/f1reZNdTX0ZAFUVwEAsSoVUDklBgqvxxxzjI0fP95d37t3r9WrV8/69etngwYN2u/j9+zZY9WqVXOP79GjR7bmmZqaalWqVLGUlBSrXLlyLIsLIAfV1dCQ6v38Y/MO25uL6qobISCJkQH8psGgdwt7EQAUstUjzy7Q+cWS+2KqyO7cudMWLlxogwcPDt5WunRp69ixo82dOzdbz7Fjxw7btWuXVa9ePdNp0tPT3SX0BXmhWRcAuZPuxl3dse9sVv+e0WpfaI39rFZ1q2rc1QrWsGZoaK1gSVmc1YrfY/8obTHVOgAUQ3sLOHvFMr+YguzGjRtdRbV27dpht+v60qVLs/UcAwcOtAMPPNCF38yMGDHChg0bluH2DRs2WFpaWiyLDJTo6uqmHbvtt81p9tumtH0/N6fZms1ptjZ1Z0zV1cRypa1+tQQ7uFqC++ku1RPsoKrxllCmdMTUe8zSUm0Dv6rFQtNqBFmgpEtOTi7Q+W3durVojlowcuRImz59uuubVb9sZlTxVR9uaEVW7Qu1atWitQCIkL7bGxkgvLqq/29Ny//qKoq3JZt534GSLinKMVD5KauMmKsgW7NmTYuLi7P169eH3a7rderUyfKxDz30kAuyH3/8sbVo0SLLaePj490lktoYdAFKcu/q//pW950sINbe1Qrl4vaNteqF1X8PtlJ4TSgbl58vAz601wiyQElXuoCzVyzziynIlitXzg2fNXv2bOvatWuwj0HX+/btm+njHnzwQRs+fLh98MEH1qZNm1hmCZTM6mryNldRXRFyZqucVFdDT8GqnwquVFcBAMVFzK0F2uXfs2dPF0g1FqyG39q+fbv16tXL3a+RCDSslvpcRcNtDRkyxKZNm2YNGjRwY89KxYoV3QUoidXVjdt2RowKsC+4/r6J6ioAAPkWZLt16+YOulI4VSht1aqVzZo1K3gA2Jo1a8JKwo8//rgb7eDCCy8Me56hQ4faPffcE+vsgQKxZ2/A5q/aZMlb0yypUoK1bVjd4kqXylF11WsByNPq6r8nC6C6CgAoyWIeR7YwFNY4soyfCKCgx08satgOAlhdhMeR5cgpAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAAL5EkAUAAIAvEWQBAADgSwRZAAAA+BJBFgAAACUnyE6YMMEaNGhgCQkJ1q5dO5s/f36W07/88svWpEkTN/2RRx5p7733Xk6XFwAAAMhZkJ0xY4YNGDDAhg4daosWLbKWLVta586dLTk5Oer0X331lXXv3t169+5tixcvtq5du7rLjz/+GOusAQAAgJwH2TFjxlifPn2sV69e1qxZM5s0aZIlJiba1KlTo04/btw4O+OMM+y2226zpk2b2n333WdHH320jR8/PtZZAwAAAEFlLAY7d+60hQsX2uDBg4O3lS5d2jp27Ghz586N+hjdrgpuKFVw33jjjUznk56e7i6elJQU93PLli22d+9eKzDp2wtuXgCKJG13SjS2g0CJt6WAt4OpqanuZyAQyNsgu3HjRtuzZ4/Vrl077HZdX7p0adTHrFu3Lur0uj0zI0aMsGHDhmW4vX79+rEsLgDkWrWxrEQAJVu1QtoObt261apUqZJ3QbagqOIbWsVVFXbTpk1Wo0YNK1WqVKEuG0oOfSOsV6+e/f7771a5cuXCXhwAKHBsB1EYVIlViD3wwAP3O21MQbZmzZoWFxdn69evD7td1+vUqRP1Mbo9luklPj7eXUJVrVo1lkUF8oxCLEEWQEnGdhAFbX+V2Bwd7FWuXDlr3bq1zZ49O6xaquvt27eP+hjdHjq9fPTRR5lODwAAAGRHzK0F2uXfs2dPa9OmjbVt29bGjh1r27dvd6MYSI8ePaxu3bquz1VuuukmO+mkk+zhhx+2s88+26ZPn24LFiywJ598MtZZAwAAADkPst26dbMNGzbYkCFD3AFbrVq1slmzZgUP6FqzZo0bycBz3HHH2bRp0+yuu+6yO+64ww499FA3YkHz5s1jnTVQoNTeovGSI9tcAKCkYDuIoq5UIDtjGwAAAADF4RS1AAAAQGEjyAIAAMCXCLIAAADwJYIsCk2DBg3cqBc59cwzzzC+cD6tWwAlk05Ff8ghh9hXX33lrq9evdqdiOjbb7+1ouSSSy5xoyEBBFlEdeWVV1rXrl3zde188803ds011+Q4mGkEjV9++SVXQVgbaF000sYBBxzgnlMjb/hdLOsWQNHb3r7yyiuWkJAQDGuaRtuqkSNHhk2nUYBCz3g5Z84cd/2II45wp5SPPLGQtntZmTRpkjVs2NCNOJTfli9fbpUqVcpQkPjpp5/sP//5j9vu67VE+1KukZCGDx9uKSkp+b6cKNoIsig0tWrVssTExBw/vnz58paUlJTrs9WsXbvW/vzzT3v11Vdt2bJldtFFF1l+27VrV5FetwAKz1NPPWWXXXaZPf7443bLLbcEb1ewHTVqlG3evHm/z7Fy5Up77rnnYpqvBjEaP3689e7d2wpiG9i9e3c74YQTMty3Y8cOa9SokQvtmZ0FVEN4Nm7c2F544YV8X1YUbQRZ5Mhnn33mToihMQZVyRw0aJDt3r07eL/OkawNcYUKFdz9jzzyiJ188sl28803R62yagN6zz332MEHH+yeU+dXvvHGG919etxvv/1m/fv3D1ZQM2stePvtt+2YY45xG3ydUvn888/P8nXoubSh1DKqAqEN+Pz58935xT1vvvmmHX300e45tXEdNmxY2GtdunSpHX/88e7+Zs2a2ccff+yeV5WS0F1zM2bMcCcH0XQvvvhi8A9W06ZN3W1NmjSxiRMnhu3i69u3r1s23V+/fv3giUayWl+R61ZUZT7vvPOsYsWKLrxffPHFYaeO1nNpTOjnn3/ePVanBtSuO72PAArOgw8+aP369XMnD/JONOTp2LGj215524Gs6Dk0DnZ6enq2571w4UJbsWKFO3lRZlTlveqqq9z2Kjd7r1RR1XNoWxRJ2/DRo0e7bVBW43h36dLFrSeUbARZxEzVy7POOsttbL777jtXNZgyZYrdf//9YWeA+/LLL+2tt95ypyT+/PPPbdGiRZk+p6qhCrtPPPGE/frrry4EHnnkke6+1157zQ466CC79957XfVUl2jeffddF1y1bIsXL3anRlbYzq7k5GR7/fXXLS4uzl1Ey62z1ekMdT///LNbPgVo7dLyNuraJajq57x589wZ6+68886oz6+wr+dZsmSJde7c2YVZnVhEz6XbHnjgAbv77rvt2WefddM/+uijbv3NnDnTVYo1vULm/tZXJJ1GWiF206ZN7guI3g9Va9RGEUp/wPQ877zzjrto2sjdmADyz8CBA+2+++5zv3/RvoRru6TtxGOPPWZ//PFHls+looG+cGva7NL27rDDDnO7+6NRKNYeK/XLalp9kZYzzzzTfUnO7KI2h1CffPKJvfzyyzZhwgTLDW3fVXiIJayj+In5zF6Aqob16tVzu6BUadS36r/++stthBXMdMpihTGd0e20005zK+zpp592VcPM6Ju9Kg2qOJQtW9ZtIL0QWr16dbcB18Y1s91MokCob/CqmHpatmyZ5Rum/iptaFXh1O4sUWVTlWTRcymA6rTMooqs/tDcfvvtrtqhUKgAqL40b9m0HKeffnrUPywXXHBB8Loer/437zb1pXlhWfPTOtGZ8FTt1XpWRTY76yuSAv0PP/xgq1atcu+baJej/riol1ZfSLzAq5Du/RG74oor3GO90A4g/7z//vtu749+50499dRMp1PA1d4TbT9UQMiMvlxrGp1Rs0+fPm4vy/5oz1dm2+lt27a5Sq1C46effhr2fNqz9M8//2T6vNpGef7++2/X76uWAO0dyg0tq/Zc6SyjodtHlCxUZBEzVQ/bt28fdoBBhw4d3IZOVQJV+9T/FBqstNE7/PDDM31OfcvXhlBBURtdVUZDd99nh6oEXnDOLoU2PW7BggUuVKqFIDS4qeKsSnBodUHLp6qwgq8qpQqHoQE7s0DZpk2b4P8V9hWA1coQ+tyqaut20cZey6b1pnD94Ycf5mh96f3SMnohVtQCobYM3edRtTe0EqOWBlWpAeS/Fi1auN9BhU9tS7OiPlkVC0J/f6PR9qVGjRpu+uzQNkVtTNGon1XbLW2HIkNx3bp13UgHmV1CQ6a2V5deeqmdeOKJlls6TkK8IgRKJoIsigSFLIVCVXu1cbr++uvdhi6Wg6K8jVosNFqBNrTqU1U7xLHHHmvXXXdd8H79QVFVVoHSu6i6qd35mW3wM+NVeb3nlcmTJ4c9948//mhff/21u0+hWlVUVYD1B0a9ZBdeeGGera+sqiaiLyqq0gLIfwqD2rOj1q0zzjgjy/50/a6rPWnw4MFZPmeZMmXcF/Nx48a5vWb7o+MKMjuQTC1b33//vc2dOzfDfbG0Fqit4KGHHnLLpovCtvaM6f9Tp061WKhdyju4FSUXrQWImUKfejS1O96ryqofVtU89bJWq1bNhSLttvZ6qLSh0lBZWX0LVyBT874uN9xwg2tZUGhUoCtXrlyGoWSiVTS0Wy7yAIlYqI1AR8LqwDLNVxcFRoXdaFQt/f33392BU7Vr13a36XXvj6bVbjFVr3VQXGa06029rLooxOoPnDbearfIan1Fvl9aRl28qqxaGLZs2eIqswCKBlUu1Zt+yimnuN/1WbNmZdqvqv51tRhktafL23ujA6dCW64yc9RRR7ljHkK37R59wddIAeeee647HkEHruaktUBBOHRbrnYKVYw1bq3CfCz0xV9/cxTAUXIRZJEphc/IQbC1m0rVPx0Rr6NidVS9gp52h6miqQqnNrzq8bzttttc4NIQWbpf90VuHD3qzdTGrV27dq63S/1TCmreLintcvvvf/8bPIo12oZL81BrgYKoptOu9vfee8/17maXgp560NTrqwMu9POcc85xgVxBUq9B7QbagKoNQL2wmp9er442VhVFR+NKZq/Voz8sahnQbjr90VLvmVocVBHRuhwzZozbva8/LpqvDo5QC4M3FmRW6yuU+mh1IJgCs943rRe9h/pDFNruAKDwaRukyqzCrKquCrPRekm932kdFLo/Cr16rv3RPLW3SOO4KrRG0jZf2x1tE9XTq/59iSWA6ot1KG3ztH0LnZ/6XvVl2/u/qtT6W6TqbmhRQQecderUKdvzRjEVAKLo2bNnQB+PyEvv3r3d/XPmzAkcc8wxgXLlygXq1KkTGDhwYGDXrl3Bx6empgYuvfTSQGJiort/zJgxgbZt2wYGDRoUnKZ+/fqBRx55xP3/9ddfD7Rr1y5QuXLlQIUKFQLHHnts4OOPPw5OO3fu3ECLFi0C8fHxbjnk6aefDlSpUiVsuV999dVAq1at3HLVrFkzcMEFF2T6/kZ7vDcvzWPevHnu+qxZswLHHXdcoHz58m759DqefPLJ4PRLliwJdOjQwc2zSZMmgbfffts9Xo+TVatWueuLFy/OMK8XX3wxuLzVqlULnHjiiYHXXnvN3ad56D6tD833tNNOCyxatChb6yt03cpvv/0WOPfcc920lSpVClx00UWBdevWBe8fOnRooGXLlmHLpsfreQDk//b2vPPOC7vtjz/+CBx66KHudzslJSXqNNq2aNsR+qf8008/ddc3b94cNm2nTp3c7druZeXiiy8O205H2349/PDDbjvy5ZdfBnIr2nbYm2fk5aSTTgpO888//7jHaXuNkq2U/insMI3iTwcJ6Fu7DqgqiMG2C5PaLFSp0FlrVK0FAL9QH6z2NOmgU1VAiyq1QOgg19CDYFEy0VqAfKFxXHWiAB3BrxYFHfkvGs+0uNHGVBt8DZWl8KqxYjWKAyEWgN/oWAP1rOpA08zGpi4K1Hcbyxi5KL4Issg3OjJV/bM6UKt169aun6k4NuWrL1Z9uBrbVa9PPane+dEBwG809F9Rd/XVVxf2IqCIoLUAAAAAvsQ4sgAAAPAlgiwAAAB8iSALAAAAXyLIAgAAwJcIsgAAAPAlgiwAAAB8iSALAAAAXyLIAgAAwJcIsgAAADA/+n9pNpMvOT3J7QAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 123
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.635838700Z",
     "start_time": "2026-04-26T14:22:32.631802200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "\n",
    "# Confusion matrices (Test set)\n",
    "# If there are many install classes, the full confusion matrix becomes unreadable.\n",
    "# So I show the confusion matrix for the most frequent classes in the test set.\n"
   ],
   "id": "51a7ab76e87a33c6",
   "outputs": [],
   "execution_count": 124
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.897864100Z",
     "start_time": "2026-04-26T14:22:32.635838700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "top_n_c = 12\n",
    "\n",
    "top_classes_c = pd.Series(y_test_c).value_counts().head(top_n_c).index.tolist()\n",
    "mask_c = pd.Series(y_test_c).isin(top_classes_c)\n",
    "\n",
    "y_test_c_top = pd.Series(y_test_c).reset_index(drop=True)[mask_c.reset_index(drop=True)]\n",
    "logreg_c_pred_top = pd.Series(logreg_c_test_pred).reset_index(drop=True)[mask_c.reset_index(drop=True)]\n",
    "knn_c_pred_top = pd.Series(knn_c_test_pred).reset_index(drop=True)[mask_c.reset_index(drop=True)]\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "cm_log_top = confusion_matrix(y_test_c_top, logreg_c_pred_top, labels=top_classes_c)\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm_log_top, display_labels=top_classes_c).plot(\n",
    "    ax=ax[0], cmap='Blues', xticks_rotation=45, values_format='d', colorbar=False\n",
    ")\n",
    "ax[0].set_title(f\"Part C: Logistic Regression Confusion Matrix (Top {top_n_c} classes, Test)\")\n",
    "\n",
    "cm_knn_top = confusion_matrix(y_test_c_top, knn_c_pred_top, labels=top_classes_c)\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm_knn_top, display_labels=top_classes_c).plot(\n",
    "    ax=ax[1], cmap='Greens', xticks_rotation=45, values_format='d', colorbar=False\n",
    ")\n",
    "ax[1].set_title(f\"Part C: KNN Confusion Matrix (Top {top_n_c} classes, Test, k={best_k_c})\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "b3f55990f53222cd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 125
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.916355800Z",
     "start_time": "2026-04-26T14:22:32.898864500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --------------------\n",
    "# Display the actual predicted outputs (so the marker can see what each model predicted)\n",
    "# --------------------\n"
   ],
   "id": "ae59b1c4d61d77fc",
   "outputs": [],
   "execution_count": 126
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.926688100Z",
     "start_time": "2026-04-26T14:22:32.916355800Z"
    }
   },
   "cell_type": "code",
   "source": [
    "logreg_c_probs = logreg_c.predict_proba(X_test_c_scaled)\n",
    "logreg_c_pred_df = prediction_sheet(y_test_c, logreg_c_test_pred, logreg_c_probs, 'Installs_Num', head_n=20)\n",
    "logreg_c_pred_df"
   ],
   "id": "1f20964c6c1d3861",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   True_Installs_Num Predicted_Installs_Num  Predicted_Confidence  Correct\n",
       "0            1000000                   5000              0.197939    False\n",
       "1            1000000                  50000              0.469158    False\n",
       "2           10000000               50000000              0.425556    False\n",
       "3             500000                1000000              0.145711    False\n",
       "4          100000000              500000000              0.470446    False\n",
       "5          100000000               50000000              0.400632    False\n",
       "6           10000000               50000000              0.492270    False\n",
       "7           10000000                5000000              0.250524    False\n",
       "8              10000                    500              0.647713    False\n",
       "9            1000000                1000000              0.171517     True\n",
       "10            100000                 500000              0.334840    False\n",
       "11             10000                  50000              0.212020    False\n",
       "12           5000000                    100              0.291118    False\n",
       "13           1000000                 500000              0.241188    False\n",
       "14              5000                    100              0.207994    False\n",
       "15            500000                  50000              0.244060    False\n",
       "16           5000000               10000000              0.237635    False\n",
       "17          10000000               50000000              0.373763    False\n",
       "18           1000000                1000000              0.196770     True\n",
       "19            100000                5000000              0.161215    False"
      ],
      "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_Installs_Num</th>\n",
       "      <th>Predicted_Installs_Num</th>\n",
       "      <th>Predicted_Confidence</th>\n",
       "      <th>Correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.197939</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000000</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.469158</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000000</td>\n",
       "      <td>50000000</td>\n",
       "      <td>0.425556</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.145711</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000000</td>\n",
       "      <td>500000000</td>\n",
       "      <td>0.470446</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100000000</td>\n",
       "      <td>50000000</td>\n",
       "      <td>0.400632</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10000000</td>\n",
       "      <td>50000000</td>\n",
       "      <td>0.492270</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10000000</td>\n",
       "      <td>5000000</td>\n",
       "      <td>0.250524</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10000</td>\n",
       "      <td>500</td>\n",
       "      <td>0.647713</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.171517</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100000</td>\n",
       "      <td>500000</td>\n",
       "      <td>0.334840</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10000</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.212020</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5000000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.291118</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1000000</td>\n",
       "      <td>500000</td>\n",
       "      <td>0.241188</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.207994</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>500000</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.244060</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5000000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>0.237635</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10000000</td>\n",
       "      <td>50000000</td>\n",
       "      <td>0.373763</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.196770</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100000</td>\n",
       "      <td>5000000</td>\n",
       "      <td>0.161215</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 127
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.941786900Z",
     "start_time": "2026-04-26T14:22:32.926688100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "knn_c_probs = knn_final_c.predict_proba(X_test_c_scaled_inner)\n",
    "knn_c_pred_df = prediction_sheet(y_test_c, knn_c_test_pred, knn_c_probs, 'Installs_Num', head_n=20)\n",
    "knn_c_pred_df"
   ],
   "id": "948657d95acea7a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   True_Installs_Num Predicted_Installs_Num  Predicted_Confidence  Correct\n",
       "0            1000000                1000000              0.293952     True\n",
       "1            1000000                1000000              0.446849     True\n",
       "2           10000000                1000000              0.522130    False\n",
       "3             500000                1000000              0.287865    False\n",
       "4          100000000                1000000              0.376263    False\n",
       "5          100000000              100000000              0.995326     True\n",
       "6           10000000               10000000              0.459153     True\n",
       "7           10000000                1000000              0.304257    False\n",
       "8              10000                 100000              0.291979    False\n",
       "9            1000000                 100000              0.427634    False\n",
       "10            100000                 500000              0.259322    False\n",
       "11             10000                  50000              0.322032    False\n",
       "12           5000000                1000000              0.234653    False\n",
       "13           1000000                 500000              0.431720    False\n",
       "14              5000                 100000              0.377805    False\n",
       "15            500000                  50000              0.431116    False\n",
       "16           5000000                1000000              0.443130    False\n",
       "17          10000000               10000000              0.353770     True\n",
       "18           1000000                 100000              0.376758    False\n",
       "19            100000                1000000              0.595938    False"
      ],
      "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_Installs_Num</th>\n",
       "      <th>Predicted_Installs_Num</th>\n",
       "      <th>Predicted_Confidence</th>\n",
       "      <th>Correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.293952</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.446849</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.522130</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.287865</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.376263</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100000000</td>\n",
       "      <td>100000000</td>\n",
       "      <td>0.995326</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10000000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>0.459153</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.304257</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10000</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.291979</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.427634</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>100000</td>\n",
       "      <td>500000</td>\n",
       "      <td>0.259322</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10000</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.322032</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.234653</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1000000</td>\n",
       "      <td>500000</td>\n",
       "      <td>0.431720</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5000</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.377805</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>500000</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.431116</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.443130</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10000000</td>\n",
       "      <td>10000000</td>\n",
       "      <td>0.353770</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1000000</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.376758</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>100000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.595938</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 128
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:32.947794900Z",
     "start_time": "2026-04-26T14:22:32.941786900Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Visual: compare distribution of predicted labels vs actual labels on the test set\n",
    "# Keep it readable by focusing on the most frequent install classes\n"
   ],
   "id": "97b114df88250967",
   "outputs": [],
   "execution_count": 129
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:33.069212300Z",
     "start_time": "2026-04-26T14:22:32.948794Z"
    }
   },
   "cell_type": "code",
   "source": [
    "dist_top_n_c = 12\n",
    "most_common_classes_c = pd.Series(y_test_c).value_counts().head(dist_top_n_c).index\n",
    "\n",
    "actual_counts_c = pd.Series(y_test_c).value_counts().reindex(most_common_classes_c)\n",
    "logreg_pred_counts_c = pd.Series(logreg_c_test_pred).value_counts().reindex(most_common_classes_c, fill_value=0)\n",
    "knn_pred_counts_c = pd.Series(knn_c_test_pred).value_counts().reindex(most_common_classes_c, fill_value=0)\n",
    "\n",
    "x = np.arange(len(most_common_classes_c))\n",
    "width = 0.28\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "ax.bar(x - width, actual_counts_c.values, width, label=\"Actual (Test)\")\n",
    "ax.bar(x,         logreg_pred_counts_c.values, width, label=\"LogReg Predictions\")\n",
    "ax.bar(x + width, knn_pred_counts_c.values, width, label=f\"KNN Predictions (k={best_k_c})\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(most_common_classes_c, rotation=45, ha='right')\n",
    "ax.set_title(f\"Part C: Actual vs Predicted Installs_Num distribution (Top {dist_top_n_c} classes, Test set)\")\n",
    "ax.set_ylabel(\"Number of apps\")\n",
    "ax.grid(True, axis='y', alpha=0.3)\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "4b9d2236b6721b38",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 130
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:33.086835200Z",
     "start_time": "2026-04-26T14:22:33.071212500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --------------------\n",
    "# Decision + short discussion (based on the real metrics above)\n",
    "# --------------------"
   ],
   "id": "c0cd0cf13f562969",
   "outputs": [],
   "execution_count": 131
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:33.092961500Z",
     "start_time": "2026-04-26T14:22:33.087836500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "best_model_c = (\n",
    "    f\"KNN (k={best_k_c})\" if knn_c_test_f1 > logreg_c_test_f1 else \"Logistic Regression\"\n",
    ")"
   ],
   "id": "d475aa4180f2014e",
   "outputs": [],
   "execution_count": 132
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-26T14:22:33.105337900Z",
     "start_time": "2026-04-26T14:22:33.093959500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "part_c_final_summary = pd.DataFrame([\n",
    "    {'Model': 'Logistic Regression', 'Accuracy': logreg_c_test_acc, 'Weighted F1': logreg_c_test_f1},\n",
    "    {'Model': f'KNN (k={best_k_c})', 'Accuracy': knn_c_test_acc, 'Weighted F1': knn_c_test_f1},\n",
    "]).sort_values('Weighted F1', ascending=False).reset_index(drop=True)\n",
    "\n",
    "display(part_c_final_summary)\n",
    "display(pd.DataFrame([{'Part C Best Model': best_model_c}]))\n"
   ],
   "id": "d0a4f6fb6c77d787",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 Model  Accuracy  Weighted F1\n",
       "0           KNN (k=41)  0.324324     0.285951\n",
       "1  Logistic Regression  0.171171     0.182992"
      ],
      "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Weighted F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN (k=41)</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.285951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.171171</td>\n",
       "      <td>0.182992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "  Part C Best Model\n",
       "0        KNN (k=41)"
      ],
      "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>Part C Best Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN (k=41)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 133
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}