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@@ -0,0 +1,2327 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LAB | Hyperparameter Tuning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Load the data**\n",
+    "\n",
+    "Finally step in order to maximize the performance on your Spaceship Titanic model.\n",
+    "\n",
+    "The data can be found here:\n",
+    "\n",
+    "https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv\n",
+    "\n",
+    "Metadata\n",
+    "\n",
+    "https://github.com/data-bootcamp-v4/data/blob/main/spaceship_titanic.md"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far we've been training and evaluating models with default values for hyperparameters.\n",
+    "\n",
+    "Today we will perform the same feature engineering as before, and then compare the best working models you got so far, but now fine tuning it's hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>HomePlanet</th>\n",
+       "      <th>CryoSleep</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Destination</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>VIP</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Transported</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0001_01</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>B/0/P</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Maham Ofracculy</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0002_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>F/0/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>Juanna Vines</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0003_01</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A/0/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>3576.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6715.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>Altark Susent</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0003_02</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A/0/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1283.0</td>\n",
+       "      <td>371.0</td>\n",
+       "      <td>3329.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>Solam Susent</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0004_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>F/1/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>303.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Willy Santantines</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
+       "0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n",
+       "1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n",
+       "2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n",
+       "3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n",
+       "4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n",
+       "\n",
+       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
+       "0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n",
+       "1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n",
+       "2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n",
+       "3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n",
+       "4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n",
+       "\n",
+       "   Transported  \n",
+       "0        False  \n",
+       "1         True  \n",
+       "2        False  \n",
+       "3        False  \n",
+       "4         True  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spaceship = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv\")\n",
+    "df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv\")\n",
+    "spaceship.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 8693 entries, 0 to 8692\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   PassengerId   8693 non-null   str    \n",
+      " 1   HomePlanet    8492 non-null   str    \n",
+      " 2   CryoSleep     8476 non-null   object \n",
+      " 3   Cabin         8494 non-null   str    \n",
+      " 4   Destination   8511 non-null   str    \n",
+      " 5   Age           8514 non-null   float64\n",
+      " 6   VIP           8490 non-null   object \n",
+      " 7   RoomService   8512 non-null   float64\n",
+      " 8   FoodCourt     8510 non-null   float64\n",
+      " 9   ShoppingMall  8485 non-null   float64\n",
+      " 10  Spa           8510 non-null   float64\n",
+      " 11  VRDeck        8505 non-null   float64\n",
+      " 12  Name          8493 non-null   str    \n",
+      " 13  Transported   8693 non-null   bool   \n",
+      "dtypes: bool(1), float64(6), object(2), str(5)\n",
+      "memory usage: 891.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId       0\n",
+       "HomePlanet      201\n",
+       "CryoSleep       217\n",
+       "Cabin           199\n",
+       "Destination     182\n",
+       "Age             179\n",
+       "VIP             203\n",
+       "RoomService     181\n",
+       "FoodCourt       183\n",
+       "ShoppingMall    208\n",
+       "Spa             183\n",
+       "VRDeck          188\n",
+       "Name            200\n",
+       "Transported       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HomePlanet</th>\n",
+       "      <th>CryoSleep</th>\n",
+       "      <th>Destination</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>VIP</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "      <th>Transported</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>3576.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6715.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1283.0</td>\n",
+       "      <td>371.0</td>\n",
+       "      <td>3329.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>303.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  HomePlanet CryoSleep  Destination   Age    VIP  RoomService  FoodCourt  \\\n",
+       "0     Europa     False  TRAPPIST-1e  39.0  False          0.0        0.0   \n",
+       "1      Earth     False  TRAPPIST-1e  24.0  False        109.0        9.0   \n",
+       "2     Europa     False  TRAPPIST-1e  58.0   True         43.0     3576.0   \n",
+       "3     Europa     False  TRAPPIST-1e  33.0  False          0.0     1283.0   \n",
+       "4      Earth     False  TRAPPIST-1e  16.0  False        303.0       70.0   \n",
+       "\n",
+       "   ShoppingMall     Spa  VRDeck  Transported  \n",
+       "0           0.0     0.0     0.0        False  \n",
+       "1          25.0   549.0    44.0         True  \n",
+       "2           0.0  6715.0    49.0        False  \n",
+       "3         371.0  3329.0   193.0        False  \n",
+       "4         151.0   565.0     2.0         True  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.drop(\n",
+    "    columns=[\n",
+    "        \"PassengerId\",\n",
+    "        \"Cabin\",\n",
+    "        \"Name\"\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HomePlanet      0\n",
+       "CryoSleep       0\n",
+       "Destination     0\n",
+       "Age             0\n",
+       "VIP             0\n",
+       "RoomService     0\n",
+       "FoodCourt       0\n",
+       "ShoppingMall    0\n",
+       "Spa             0\n",
+       "VRDeck          0\n",
+       "Transported     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define numerical and categorical columns\n",
+    "numerical_columns = [\n",
+    "    \"Age\",\n",
+    "    \"RoomService\",\n",
+    "    \"FoodCourt\",\n",
+    "    \"ShoppingMall\",\n",
+    "    \"Spa\",\n",
+    "    \"VRDeck\"\n",
+    "]\n",
+    "\n",
+    "categorical_columns = [\n",
+    "    \"HomePlanet\",\n",
+    "    \"CryoSleep\",\n",
+    "    \"Destination\",\n",
+    "    \"VIP\"\n",
+    "]\n",
+    "\n",
+    "# Fill missing numerical values with the median\n",
+    "for column in numerical_columns:\n",
+    "    df[column] = df[column].fillna(df[column].median())\n",
+    "\n",
+    "# Fill missing categorical values with the mode\n",
+    "for column in categorical_columns:\n",
+    "    df[column] = df[column].fillna(df[column].mode()[0])\n",
+    "\n",
+    "# Verify missing values\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape: (8693, 10)\n",
+      "y shape: (8693,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = df.drop(columns=[\"Transported\"])\n",
+    "y = df[\"Transported\"]\n",
+    "\n",
+    "print(\"X shape:\", X.shape)\n",
+    "print(\"y shape:\", y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape after encoding: (8693, 12)\n",
+      "\n",
+      "Columns:\n",
+      "['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'HomePlanet_Europa', 'HomePlanet_Mars', 'CryoSleep_True', 'Destination_PSO J318.5-22', 'Destination_TRAPPIST-1e', 'VIP_True']\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(\n",
+    "    X,\n",
+    "    columns=[\n",
+    "        \"HomePlanet\",\n",
+    "        \"CryoSleep\",\n",
+    "        \"Destination\",\n",
+    "        \"VIP\"\n",
+    "    ],\n",
+    "    drop_first=True\n",
+    ")\n",
+    "\n",
+    "print(\"X shape after encoding:\", X.shape)\n",
+    "print(\"\\nColumns:\")\n",
+    "print(X.columns.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set: (6954, 12)\n",
+      "Test set: (1739, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X,\n",
+    "    y,\n",
+    "    test_size=0.20,\n",
+    "    random_state=42,\n",
+    "    stratify=y\n",
+    ")\n",
+    "\n",
+    "print(\"Training set:\", X_train.shape)\n",
+    "print(\"Test set:\", X_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now perform the same as before:\n",
+    "- Feature Scaling\n",
+    "- Feature Selection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "      <th>HomePlanet_Europa</th>\n",
+       "      <th>HomePlanet_Mars</th>\n",
+       "      <th>CryoSleep_True</th>\n",
+       "      <th>Destination_PSO J318.5-22</th>\n",
+       "      <th>Destination_TRAPPIST-1e</th>\n",
+       "      <th>VIP_True</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3600</th>\n",
+       "      <td>-1.996149</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.305732</td>\n",
+       "      <td>-0.267311</td>\n",
+       "      <td>-0.264632</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1262</th>\n",
+       "      <td>-0.811703</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.305732</td>\n",
+       "      <td>-0.267311</td>\n",
+       "      <td>-0.264632</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8612</th>\n",
+       "      <td>0.442416</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.305732</td>\n",
+       "      <td>-0.267311</td>\n",
+       "      <td>-0.264632</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5075</th>\n",
+       "      <td>-0.184643</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.305732</td>\n",
+       "      <td>-0.267311</td>\n",
+       "      <td>-0.264632</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4758</th>\n",
+       "      <td>-1.090396</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.195161</td>\n",
+       "      <td>-0.266427</td>\n",
+       "      <td>4.423937</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Age  RoomService  FoodCourt  ShoppingMall       Spa    VRDeck  \\\n",
+       "3600 -1.996149     -0.33634  -0.279076     -0.305732 -0.267311 -0.264632   \n",
+       "1262 -0.811703     -0.33634  -0.279076     -0.305732 -0.267311 -0.264632   \n",
+       "8612  0.442416     -0.33634  -0.279076     -0.305732 -0.267311 -0.264632   \n",
+       "5075 -0.184643     -0.33634  -0.279076     -0.305732 -0.267311 -0.264632   \n",
+       "4758 -1.090396     -0.33634  -0.279076     -0.195161 -0.266427  4.423937   \n",
+       "\n",
+       "      HomePlanet_Europa  HomePlanet_Mars  CryoSleep_True  \\\n",
+       "3600              False            False           False   \n",
+       "1262              False            False            True   \n",
+       "8612              False            False           False   \n",
+       "5075               True            False            True   \n",
+       "4758              False            False           False   \n",
+       "\n",
+       "      Destination_PSO J318.5-22  Destination_TRAPPIST-1e  VIP_True  \n",
+       "3600                      False                     True     False  \n",
+       "1262                      False                     True     False  \n",
+       "8612                       True                    False     False  \n",
+       "5075                      False                    False     False  \n",
+       "4758                      False                     True     False  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "\n",
+    "X_train_scaled = X_train.copy()\n",
+    "X_test_scaled = X_test.copy()\n",
+    "\n",
+    "X_train_scaled[numerical_columns] = scaler.fit_transform(\n",
+    "    X_train[numerical_columns]\n",
+    ")\n",
+    "\n",
+    "X_test_scaled[numerical_columns] = scaler.transform(\n",
+    "    X_test[numerical_columns]\n",
+    ")\n",
+    "\n",
+    "X_train_scaled.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected features:\n",
+      "['Age', 'RoomService', 'FoodCourt', 'Spa', 'VRDeck', 'HomePlanet_Europa', 'CryoSleep_True', 'Destination_TRAPPIST-1e']\n",
+      "\n",
+      "Training shape: (6954, 8)\n",
+      "Test shape: (1739, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "selector = SelectKBest(\n",
+    "    score_func=f_classif,\n",
+    "    k=8\n",
+    ")\n",
+    "\n",
+    "X_train_selected = selector.fit_transform(\n",
+    "    X_train_scaled,\n",
+    "    y_train\n",
+    ")\n",
+    "\n",
+    "X_test_selected = selector.transform(\n",
+    "    X_test_scaled\n",
+    ")\n",
+    "\n",
+    "selected_features = X_train_scaled.columns[\n",
+    "    selector.get_support()\n",
+    "]\n",
+    "\n",
+    "print(\"Selected features:\")\n",
+    "print(selected_features.tolist())\n",
+    "\n",
+    "print(\"\\nTraining shape:\", X_train_selected.shape)\n",
+    "print(\"Test shape:\", X_test_selected.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Now let's use the best model we got so far in order to see how it can improve when we fine tune it's hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.sk-global {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       ".sk-global.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       ".sk-global.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       ".sk-global {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".sk-global pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip-path: inset(100%);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       ".sk-global div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       ".sk-global div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       ".sk-global div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       ".sk-global label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label label.sk-toggleable__label,\n",
+       ".sk-global div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       ".sk-global div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       ".sk-global div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       ".sk-global div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       ".sk-global a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       ".sk-global a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       ".sk-global a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       ".sk-global a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-top-container.sk-global {\n",
+       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
+       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
+       "   to allow keyboard-only users to navigate the display. */\n",
+       "  overflow-x: scroll !important;\n",
+       "  max-width: 100%;\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    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 td {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table :is(td, th) {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    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, .estimator-table th {\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",
+       "td.fitted-att-type {\n",
+       "    white-space: preserve nowrap;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "@supports(anchor-name: --doc-link) {\n",
+       "    a.param-doc-link,\n",
+       "    a.param-doc-link:link,\n",
+       "    a.param-doc-link:visited {\n",
+       "    anchor-name: --doc-link;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "@supports(position-area: center right) {\n",
+       "    .param-doc-description {\n",
+       "    position-area: center right;\n",
+       "    position: fixed;\n",
+       "    margin-left: 0;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".features {\n",
+       "  font-family: monospace;\n",
+       "  cursor: pointer;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: .20em;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  font-size: inherit; /* Needed for jupyter */\n",
+       "}\n",
+       "\n",
+       ".features.fitted {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features summary {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  margin-bottom: 0;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "  padding: .25em;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features.fitted details[open] > summary {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features details > summary .arrow::before {\n",
+       "  content: \"▸\";\n",
+       "  color: grey;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary .arrow::before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       ".features details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features.fitted details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features .features-container {\n",
+       "  max-width: 15em;\n",
+       "  max-height: 10em;\n",
+       "  overflow: auto;\n",
+       "  scrollbar-width: thin;\n",
+       "  padding: .25em 0.1rem;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 0 0 .5em .5em;\n",
+       "}\n",
+       "\n",
+       ".features.fitted .features-container {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features .image-container {\n",
+       "  block-size: 1em;\n",
+       "  inline-size: 1em;\n",
+       "  padding: 0;\n",
+       "  margin: 0%;\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  align-items: center;\n",
+       "}\n",
+       "\n",
+       ".features .copy-paste-icon {\n",
+       "  background-size: 1em 1em;\n",
+       "  width: 1em;\n",
+       "  height: 1em;\n",
+       "  filter: grayscale(100%) opacity(60%);\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  width: 100%;\n",
+       "  margin: 0.01em;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(odd) {\n",
+       "  background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(even) {\n",
+       "  background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:hover {\n",
+       "  background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  table-layout: inherit;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table td {\n",
+       "  text-align: left;\n",
+       "  padding: 0 0.5em;\n",
+       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "  white-space: nowrap;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".total_features {\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  margin-top: 0.5em;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42)</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 sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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/1.9/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=\"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",
+       "            style=\"anchor-name: --doc-link-random_state;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/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\"\n",
+       "            style=\"position-anchor: --doc-link-random_state;\">\n",
+       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</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",
+       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
+       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-penalty;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/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\"\n",
+       "            style=\"position-anchor: --doc-link-penalty;\">\n",
+       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: 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 &#x27;multinomial&#x27; + 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` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-C;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-C;\">\n",
+       "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
+       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 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",
+       "            style=\"anchor-name: --doc-link-dual;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-dual;\">\n",
+       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; 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",
+       "            style=\"anchor-name: --doc-link-tol;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-tol;\">\n",
+       "            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",
+       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
+       "            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",
+       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
+       "            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 &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/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\"\n",
+       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
+       "            class_weight: dict or &#x27;balanced&#x27;, 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 &quot;balanced&quot; 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=&#x27;balanced&#x27;*</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('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-solver;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/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\"\n",
+       "            style=\"position-anchor: --doc-link-solver;\">\n",
+       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `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, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; 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 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; 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 &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<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 &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; 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=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-verbose;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-verbose;\">\n",
+       "            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",
+       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
+       "            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 &lt;warm_start&gt;`.<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",
+       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
+       "            n_jobs: int, default=None<br><br>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",
+       "    \n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Fitted attributes</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                    <tbody>\n",
+       "                        <tr>\n",
+       "                        <th>Name</th>\n",
+       "                        <th>Type</th>\n",
+       "                        <th>Value</th>\n",
+       "                        </tr>\n",
+       "                        \n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-classes_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
+       "            classes_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-classes_;\">\n",
+       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[bool](2,)</td>\n",
+       "           <td>[False, True]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-coef_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
+       "            coef_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-coef_;\">\n",
+       "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[float64](1, 8)</td>\n",
+       "           <td>[[-0.09,-0.84, 0.74,..., 2.01, 1.19,-0.13]]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
+       "            intercept_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
+       "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
+       "           <td>[-1.15]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
+       "            n_features_in_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
+       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">int</td>\n",
+       "           <td>8</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
+       "            n_iter_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
+       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
+       "           <td>[12]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "                    </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
+       " SPDX-License-Identifier: BSD-3-Clause\n",
+       "*/\n",
+       "\n",
+       "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",
+       "\n",
+       "    const parent = element.parentElement;\n",
+       "    if (!parent || !parent.nextElementSibling) {\n",
+       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
+       "        return;\n",
+       "    }\n",
+       "\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",
+       " * Copy the list of feature names formatted as a Python list.\n",
+       " *\n",
+       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
+       " *   contain a `details` element and a table containing feature named.\n",
+       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
+       " */\n",
+       "function copyFeatureNamesToClipboard(element) {\n",
+       "    var detailsElem = element.closest('.features').querySelector('details');\n",
+       "    var wasOpen = detailsElem.open;\n",
+       "    detailsElem.open = true;\n",
+       "    var content = element.closest('.features').querySelector('tbody')\n",
+       "                  .innerText.trim();\n",
+       "    if (!wasOpen) detailsElem.open = false;\n",
+       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
+       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
+       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
+       "    const originalStyle = element.style;\n",
+       "    const copyMark = document.createElement('span');\n",
+       "    copyMark.innerHTML = '✔';\n",
+       "    copyMark.style.color = 'blue';\n",
+       "    copyMark.style.fontSize = '1em';\n",
+       "\n",
+       "    navigator.clipboard.writeText(formattedText)\n",
+       "        .then(() => {\n",
+       "            element.style.display = 'none';\n",
+       "            element.parentElement.appendChild(copyMark);\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                copyMark.remove();\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'orange';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        });\n",
+       "    return false;\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-1');</script></body>"
+      ],
+      "text/plain": [
+       "LogisticRegression(max_iter=1000, random_state=42)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LogisticRegression(\n",
+    "    max_iter=1000,\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "model.fit(\n",
+    "    X_train_selected,\n",
+    "    y_train\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Evaluate your model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline Accuracy: 0.7838\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(\n",
+    "    X_test_selected\n",
+    ")\n",
+    "\n",
+    "baseline_accuracy = accuracy_score(\n",
+    "    y_test,\n",
+    "    y_pred\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"Baseline Accuracy:\",\n",
+    "    round(baseline_accuracy, 4)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Grid/Random Search**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this lab we will use Grid Search."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Define hyperparameters to fine tune."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'C': [0.01, 0.1, 1, 10, 100], 'solver': ['liblinear', 'lbfgs']}"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid = {\n",
+    "    \"C\": [0.01, 0.1, 1, 10, 100],\n",
+    "    \"solver\": [\"liblinear\", \"lbfgs\"]\n",
+    "}\n",
+    "\n",
+    "param_grid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Run Grid Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters: {'C': 100, 'solver': 'liblinear'}\n",
+      "Best CV accuracy: 0.786\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid_search = GridSearchCV(\n",
+    "    estimator=LogisticRegression(\n",
+    "        max_iter=1000,\n",
+    "        random_state=42\n",
+    "    ),\n",
+    "    param_grid=param_grid,\n",
+    "    cv=5,\n",
+    "    scoring=\"accuracy\",\n",
+    "    n_jobs=-1\n",
+    ")\n",
+    "\n",
+    "grid_search.fit(\n",
+    "    X_train_selected,\n",
+    "    y_train\n",
+    ")\n",
+    "\n",
+    "print(\"Best parameters:\", grid_search.best_params_)\n",
+    "print(\"Best CV accuracy:\", round(grid_search.best_score_, 4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Evaluate your model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline Accuracy: 0.7838\n",
+      "Tuned Accuracy: 0.7844\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_model = grid_search.best_estimator_\n",
+    "\n",
+    "y_pred_tuned = best_model.predict(\n",
+    "    X_test_selected\n",
+    ")\n",
+    "\n",
+    "tuned_accuracy = accuracy_score(\n",
+    "    y_test,\n",
+    "    y_pred_tuned\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"Baseline Accuracy:\",\n",
+    "    round(baseline_accuracy, 4)\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"Tuned Accuracy:\",\n",
+    "    round(tuned_accuracy, 4)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Evaluation\n",
+    "\n",
+    "Grid Search identified the best Logistic Regression hyperparameters as `C = 100` and `solver = 'liblinear'`.\n",
+    "\n",
+    "The baseline model achieved an accuracy of 0.7838, while the tuned model achieved an accuracy of 0.7844.\n",
+    "\n",
+    "Hyperparameter tuning therefore produced a small improvement of 0.0006, equivalent to 0.06 percentage points. This indicates that the baseline Logistic Regression model was already performing close to its optimal configuration for the selected features.\n",
+    "\n",
+    "Although the improvement is small, Grid Search provides a systematic method for selecting hyperparameters using cross validation rather than relying on manually chosen settings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (lab-hyperparameter-tuning)",
+   "language": "python",
+   "name": "lab-hyperparameter-tuning"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.virtual_documents/lab-hyper-tuning.ipynb b/.virtual_documents/lab-hyper-tuning.ipynb
new file mode 100644
index 0000000..e435909
--- /dev/null
+++ b/.virtual_documents/lab-hyper-tuning.ipynb
@@ -0,0 +1,302 @@
+
+
+
+
+
+
+
+
+
+#Libraries
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+
+spaceship = pd.read_csv("https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv")
+df = pd.read_csv("https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv")
+spaceship.head()
+
+
+df.info()
+
+
+df.isnull().sum()
+
+
+# Remove identifier and high-cardinality text columns
+df = df.drop(columns=["PassengerId", "Cabin", "Name"])
+
+df.head()
+
+
+from sklearn.preprocessing import StandardScaler
+
+numerical_columns = [
+    "Age",
+    "RoomService",
+    "FoodCourt",
+    "ShoppingMall",
+    "Spa",
+    "VRDeck"
+]
+
+scaler = StandardScaler()
+
+X_train_scaled = X_train.copy()
+X_test_scaled = X_test.copy()
+
+X_train_scaled[numerical_columns] = scaler.fit_transform(
+    X_train[numerical_columns]
+)
+
+X_test_scaled[numerical_columns] = scaler.transform(
+    X_test[numerical_columns]
+)
+
+X_train_scaled.head()
+
+
+# Fill numerical missing values with the median
+for column in numerical_columns:
+    df[column] = df[column].fillna(df[column].median())
+
+# Fill categorical missing values with the mode
+for column in categorical_columns:
+    df[column] = df[column].fillna(df[column].mode()[0])
+
+
+df.isnull().sum()
+
+
+X = df.drop(columns=["Transported"])
+y = df["Transported"]
+
+print("X shape:", X.shape)
+print("y shape:", y.shape)
+
+
+X = pd.get_dummies(
+    X,
+    columns=["HomePlanet", "CryoSleep", "Destination", "VIP"],
+    drop_first=True
+)
+
+X.head()
+
+
+from sklearn.preprocessing import StandardScaler
+from sklearn.feature_selection import SelectKBest, f_classif
+
+# Numerical columns to scale
+numerical_columns = [
+    "Age",
+    "RoomService",
+    "FoodCourt",
+    "ShoppingMall",
+    "Spa",
+    "VRDeck"
+]
+
+# Feature Scaling
+scaler = StandardScaler()
+
+X_train_scaled = X_train.copy()
+X_test_scaled = X_test.copy()
+
+X_train_scaled[numerical_columns] = scaler.fit_transform(
+    X_train[numerical_columns]
+)
+
+X_test_scaled[numerical_columns] = scaler.transform(
+    X_test[numerical_columns]
+)
+
+# Feature Selection
+selector = SelectKBest(
+    score_func=f_classif,
+    k=8
+)
+
+X_train_selected = selector.fit_transform(
+    X_train_scaled,
+    y_train
+)
+
+X_test_selected = selector.transform(
+    X_test_scaled
+)
+
+selected_features = X_train_scaled.columns[
+    selector.get_support()
+]
+
+print("Selected features:")
+print(selected_features.tolist())
+
+print("\nTraining shape:", X_train_selected.shape)
+print("Test shape:", X_test_selected.shape)
+
+
+
+
+
+from sklearn.model_selection import train_test_split
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X,
+    y,
+    test_size=0.20,
+    random_state=42,
+    stratify=y
+)
+
+print("Training set:", X_train.shape)
+print("Test set:", X_test.shape)
+
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+
+log_model = LogisticRegression(
+    max_iter=1000,
+    random_state=42
+)
+
+log_model.fit(X_train_scaled, y_train)
+
+y_pred = log_model.predict(X_test_scaled)
+
+accuracy = accuracy_score(y_test, y_pred)
+
+print("Scaled Logistic Regression Accuracy:", round(accuracy, 4))
+
+
+X_train_scaled.head()
+
+
+from sklearn.feature_selection import SelectKBest, f_classif
+
+selector = SelectKBest(
+    score_func=f_classif,
+    k=8
+)
+
+X_train_selected = selector.fit_transform(
+    X_train_scaled,
+    y_train
+)
+
+X_test_selected = selector.transform(
+    X_test_scaled
+)
+
+selected_features = X_train_scaled.columns[
+    selector.get_support()
+]
+
+print("Selected features:")
+print(selected_features.tolist())
+
+print("\nTraining shape:", X_train_selected.shape)
+print("Test shape:", X_test_selected.shape)
+
+
+
+
+
+from sklearn.linear_model import LogisticRegression
+
+model = LogisticRegression(
+    max_iter=1000,
+    random_state=42
+)
+
+model.fit(X_train_selected, y_train)
+
+
+
+
+
+from sklearn.metrics import accuracy_score
+
+y_pred = model.predict(X_test_selected)
+
+accuracy = accuracy_score(y_test, y_pred)
+
+print("Baseline Accuracy:", round(accuracy, 4))
+
+
+
+
+
+
+
+
+
+
+
+param_grid = {
+    "C": [0.01, 0.1, 1, 10, 100],
+    "solver": ["liblinear", "lbfgs"]
+}
+
+param_grid
+
+
+
+
+
+from sklearn.model_selection import GridSearchCV
+
+grid_search = GridSearchCV(
+    estimator=LogisticRegression(
+        max_iter=1000,
+        random_state=42
+    ),
+    param_grid=param_grid,
+    cv=5,
+    scoring="accuracy",
+    n_jobs=-1
+)
+
+grid_search.fit(
+    X_train_selected,
+    y_train
+)
+
+print("Best parameters:", grid_search.best_params_)
+print("Best CV accuracy:", round(grid_search.best_score_, 4))
+
+
+
+
+
+best_model = grid_search.best_estimator_
+
+y_pred_tuned = best_model.predict(
+    X_test_selected
+)
+
+tuned_accuracy = accuracy_score(
+    y_test,
+    y_pred_tuned
+)
+
+print("Baseline Accuracy:", round(accuracy, 4))
+print("Tuned Accuracy:", round(tuned_accuracy, 4))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/anaconda_projects/db/project_filebrowser.db b/anaconda_projects/db/project_filebrowser.db
new file mode 100644
index 0000000..ffdfcb3
Binary files /dev/null and b/anaconda_projects/db/project_filebrowser.db differ
diff --git a/lab-hyper-tuning.ipynb b/lab-hyper-tuning.ipynb
index 847d487..5b4a1c0 100644
--- a/lab-hyper-tuning.ipynb
+++ b/lab-hyper-tuning.ipynb
@@ -35,19 +35,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Libraries\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split"
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.feature_selection import SelectKBest, f_classif\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.model_selection import GridSearchCV"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -200,116 +205,2095 @@
        "4         True  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "spaceship = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv\")\n",
+    "df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv\")\n",
     "spaceship.head()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now perform the same as before:\n",
-    "- Feature Scaling\n",
-    "- Feature Selection\n"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 8693 entries, 0 to 8692\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   PassengerId   8693 non-null   str    \n",
+      " 1   HomePlanet    8492 non-null   str    \n",
+      " 2   CryoSleep     8476 non-null   object \n",
+      " 3   Cabin         8494 non-null   str    \n",
+      " 4   Destination   8511 non-null   str    \n",
+      " 5   Age           8514 non-null   float64\n",
+      " 6   VIP           8490 non-null   object \n",
+      " 7   RoomService   8512 non-null   float64\n",
+      " 8   FoodCourt     8510 non-null   float64\n",
+      " 9   ShoppingMall  8485 non-null   float64\n",
+      " 10  Spa           8510 non-null   float64\n",
+      " 11  VRDeck        8505 non-null   float64\n",
+      " 12  Name          8493 non-null   str    \n",
+      " 13  Transported   8693 non-null   bool   \n",
+      "dtypes: bool(1), float64(6), object(2), str(5)\n",
+      "memory usage: 891.5+ KB\n"
+     ]
+    }
+   ],
    "source": [
-    "#your code here"
+    "df.info()"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 15,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId       0\n",
+       "HomePlanet      201\n",
+       "CryoSleep       217\n",
+       "Cabin           199\n",
+       "Destination     182\n",
+       "Age             179\n",
+       "VIP             203\n",
+       "RoomService     181\n",
+       "FoodCourt       183\n",
+       "ShoppingMall    208\n",
+       "Spa             183\n",
+       "VRDeck          188\n",
+       "Name            200\n",
+       "Transported       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "- Now let's use the best model we got so far in order to see how it can improve when we fine tune it's hyperparameters."
+    "df.isnull().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HomePlanet</th>\n",
+       "      <th>CryoSleep</th>\n",
+       "      <th>Destination</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>VIP</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "      <th>Transported</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>549.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>3576.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6715.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1283.0</td>\n",
+       "      <td>371.0</td>\n",
+       "      <td>3329.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>303.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>151.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  HomePlanet CryoSleep  Destination   Age    VIP  RoomService  FoodCourt  \\\n",
+       "0     Europa     False  TRAPPIST-1e  39.0  False          0.0        0.0   \n",
+       "1      Earth     False  TRAPPIST-1e  24.0  False        109.0        9.0   \n",
+       "2     Europa     False  TRAPPIST-1e  58.0   True         43.0     3576.0   \n",
+       "3     Europa     False  TRAPPIST-1e  33.0  False          0.0     1283.0   \n",
+       "4      Earth     False  TRAPPIST-1e  16.0  False        303.0       70.0   \n",
+       "\n",
+       "   ShoppingMall     Spa  VRDeck  Transported  \n",
+       "0           0.0     0.0     0.0        False  \n",
+       "1          25.0   549.0    44.0         True  \n",
+       "2           0.0  6715.0    49.0        False  \n",
+       "3         371.0  3329.0   193.0        False  \n",
+       "4         151.0   565.0     2.0         True  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#your code here"
+    "df = df.drop(\n",
+    "    columns=[\n",
+    "        \"PassengerId\",\n",
+    "        \"Cabin\",\n",
+    "        \"Name\"\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "df.head()"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HomePlanet      0\n",
+       "CryoSleep       0\n",
+       "Destination     0\n",
+       "Age             0\n",
+       "VIP             0\n",
+       "RoomService     0\n",
+       "FoodCourt       0\n",
+       "ShoppingMall    0\n",
+       "Spa             0\n",
+       "VRDeck          0\n",
+       "Transported     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "- Evaluate your model"
+    "# Define numerical and categorical columns\n",
+    "numerical_columns = [\n",
+    "    \"Age\",\n",
+    "    \"RoomService\",\n",
+    "    \"FoodCourt\",\n",
+    "    \"ShoppingMall\",\n",
+    "    \"Spa\",\n",
+    "    \"VRDeck\"\n",
+    "]\n",
+    "\n",
+    "categorical_columns = [\n",
+    "    \"HomePlanet\",\n",
+    "    \"CryoSleep\",\n",
+    "    \"Destination\",\n",
+    "    \"VIP\"\n",
+    "]\n",
+    "\n",
+    "# Fill missing numerical values with the median\n",
+    "for column in numerical_columns:\n",
+    "    df[column] = df[column].fillna(df[column].median())\n",
+    "\n",
+    "# Fill missing categorical values with the mode\n",
+    "for column in categorical_columns:\n",
+    "    df[column] = df[column].fillna(df[column].mode()[0])\n",
+    "\n",
+    "# Verify missing values\n",
+    "df.isnull().sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape: (8693, 10)\n",
+      "y shape: (8693,)\n"
+     ]
+    }
+   ],
    "source": [
-    "#your code here"
+    "X = df.drop(columns=[\"Transported\"])\n",
+    "y = df[\"Transported\"]\n",
+    "\n",
+    "print(\"X shape:\", X.shape)\n",
+    "print(\"y shape:\", y.shape)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape after encoding: (8693, 12)\n",
+      "\n",
+      "Columns:\n",
+      "['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'HomePlanet_Europa', 'HomePlanet_Mars', 'CryoSleep_True', 'Destination_PSO J318.5-22', 'Destination_TRAPPIST-1e', 'VIP_True']\n"
+     ]
+    }
+   ],
    "source": [
-    "**Grid/Random Search**"
+    "X = pd.get_dummies(\n",
+    "    X,\n",
+    "    columns=[\n",
+    "        \"HomePlanet\",\n",
+    "        \"CryoSleep\",\n",
+    "        \"Destination\",\n",
+    "        \"VIP\"\n",
+    "    ],\n",
+    "    drop_first=True\n",
+    ")\n",
+    "\n",
+    "print(\"X shape after encoding:\", X.shape)\n",
+    "print(\"\\nColumns:\")\n",
+    "print(X.columns.tolist())"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set: (6954, 12)\n",
+      "Test set: (1739, 12)\n"
+     ]
+    }
+   ],
    "source": [
-    "For this lab we will use Grid Search."
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X,\n",
+    "    y,\n",
+    "    test_size=0.20,\n",
+    "    random_state=42,\n",
+    "    stratify=y\n",
+    ")\n",
+    "\n",
+    "print(\"Training set:\", X_train.shape)\n",
+    "print(\"Test set:\", X_test.shape)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Define hyperparameters to fine tune."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#your code here"
+    "Now perform the same as before:\n",
+    "- Feature Scaling\n",
+    "- Feature Selection\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Run Grid Search"
+    "## Feature Scaling"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 21,
    "metadata": {},
-   "source": [
-    "- Evaluate your model"
-   ]
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>RoomService</th>\n",
+       "      <th>FoodCourt</th>\n",
+       "      <th>ShoppingMall</th>\n",
+       "      <th>Spa</th>\n",
+       "      <th>VRDeck</th>\n",
+       "      <th>HomePlanet_Europa</th>\n",
+       "      <th>HomePlanet_Mars</th>\n",
+       "      <th>CryoSleep_True</th>\n",
+       "      <th>Destination_PSO J318.5-22</th>\n",
+       "      <th>Destination_TRAPPIST-1e</th>\n",
+       "      <th>VIP_True</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3600</th>\n",
+       "      <td>-1.996149</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.305732</td>\n",
+       "      <td>-0.267311</td>\n",
+       "      <td>-0.264632</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1262</th>\n",
+       "      <td>-0.811703</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.305732</td>\n",
+       "      <td>-0.267311</td>\n",
+       "      <td>-0.264632</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8612</th>\n",
+       "      <td>0.442416</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.305732</td>\n",
+       "      <td>-0.267311</td>\n",
+       "      <td>-0.264632</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5075</th>\n",
+       "      <td>-0.184643</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.305732</td>\n",
+       "      <td>-0.267311</td>\n",
+       "      <td>-0.264632</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4758</th>\n",
+       "      <td>-1.090396</td>\n",
+       "      <td>-0.33634</td>\n",
+       "      <td>-0.279076</td>\n",
+       "      <td>-0.195161</td>\n",
+       "      <td>-0.266427</td>\n",
+       "      <td>4.423937</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Age  RoomService  FoodCourt  ShoppingMall       Spa    VRDeck  \\\n",
+       "3600 -1.996149     -0.33634  -0.279076     -0.305732 -0.267311 -0.264632   \n",
+       "1262 -0.811703     -0.33634  -0.279076     -0.305732 -0.267311 -0.264632   \n",
+       "8612  0.442416     -0.33634  -0.279076     -0.305732 -0.267311 -0.264632   \n",
+       "5075 -0.184643     -0.33634  -0.279076     -0.305732 -0.267311 -0.264632   \n",
+       "4758 -1.090396     -0.33634  -0.279076     -0.195161 -0.266427  4.423937   \n",
+       "\n",
+       "      HomePlanet_Europa  HomePlanet_Mars  CryoSleep_True  \\\n",
+       "3600              False            False           False   \n",
+       "1262              False            False            True   \n",
+       "8612              False            False           False   \n",
+       "5075               True            False            True   \n",
+       "4758              False            False           False   \n",
+       "\n",
+       "      Destination_PSO J318.5-22  Destination_TRAPPIST-1e  VIP_True  \n",
+       "3600                      False                     True     False  \n",
+       "1262                      False                     True     False  \n",
+       "8612                       True                    False     False  \n",
+       "5075                      False                    False     False  \n",
+       "4758                      False                     True     False  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "\n",
+    "X_train_scaled = X_train.copy()\n",
+    "X_test_scaled = X_test.copy()\n",
+    "\n",
+    "X_train_scaled[numerical_columns] = scaler.fit_transform(\n",
+    "    X_train[numerical_columns]\n",
+    ")\n",
+    "\n",
+    "X_test_scaled[numerical_columns] = scaler.transform(\n",
+    "    X_test[numerical_columns]\n",
+    ")\n",
+    "\n",
+    "X_train_scaled.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected features:\n",
+      "['Age', 'RoomService', 'FoodCourt', 'Spa', 'VRDeck', 'HomePlanet_Europa', 'CryoSleep_True', 'Destination_TRAPPIST-1e']\n",
+      "\n",
+      "Training shape: (6954, 8)\n",
+      "Test shape: (1739, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "selector = SelectKBest(\n",
+    "    score_func=f_classif,\n",
+    "    k=8\n",
+    ")\n",
+    "\n",
+    "X_train_selected = selector.fit_transform(\n",
+    "    X_train_scaled,\n",
+    "    y_train\n",
+    ")\n",
+    "\n",
+    "X_test_selected = selector.transform(\n",
+    "    X_test_scaled\n",
+    ")\n",
+    "\n",
+    "selected_features = X_train_scaled.columns[\n",
+    "    selector.get_support()\n",
+    "]\n",
+    "\n",
+    "print(\"Selected features:\")\n",
+    "print(selected_features.tolist())\n",
+    "\n",
+    "print(\"\\nTraining shape:\", X_train_selected.shape)\n",
+    "print(\"Test shape:\", X_test_selected.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Now let's use the best model we got so far in order to see how it can improve when we fine tune it's hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.sk-global {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       ".sk-global.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       ".sk-global.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       ".sk-global {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".sk-global pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip-path: inset(100%);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       ".sk-global div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       ".sk-global div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       ".sk-global div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       ".sk-global label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label label.sk-toggleable__label,\n",
+       ".sk-global div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       ".sk-global div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       ".sk-global div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       ".sk-global div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       ".sk-global a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       ".sk-global a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       ".sk-global a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       ".sk-global a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-top-container.sk-global {\n",
+       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
+       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
+       "   to allow keyboard-only users to navigate the display. */\n",
+       "  overflow-x: scroll !important;\n",
+       "  max-width: 100%;\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    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 td {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table :is(td, th) {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    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, .estimator-table th {\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",
+       "td.fitted-att-type {\n",
+       "    white-space: preserve nowrap;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "@supports(anchor-name: --doc-link) {\n",
+       "    a.param-doc-link,\n",
+       "    a.param-doc-link:link,\n",
+       "    a.param-doc-link:visited {\n",
+       "    anchor-name: --doc-link;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "@supports(position-area: center right) {\n",
+       "    .param-doc-description {\n",
+       "    position-area: center right;\n",
+       "    position: fixed;\n",
+       "    margin-left: 0;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".features {\n",
+       "  font-family: monospace;\n",
+       "  cursor: pointer;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: .20em;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  font-size: inherit; /* Needed for jupyter */\n",
+       "}\n",
+       "\n",
+       ".features.fitted {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features summary {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  margin-bottom: 0;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "  padding: .25em;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features.fitted details[open] > summary {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features details > summary .arrow::before {\n",
+       "  content: \"▸\";\n",
+       "  color: grey;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary .arrow::before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       ".features details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features.fitted details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features .features-container {\n",
+       "  max-width: 15em;\n",
+       "  max-height: 10em;\n",
+       "  overflow: auto;\n",
+       "  scrollbar-width: thin;\n",
+       "  padding: .25em 0.1rem;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 0 0 .5em .5em;\n",
+       "}\n",
+       "\n",
+       ".features.fitted .features-container {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features .image-container {\n",
+       "  block-size: 1em;\n",
+       "  inline-size: 1em;\n",
+       "  padding: 0;\n",
+       "  margin: 0%;\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  align-items: center;\n",
+       "}\n",
+       "\n",
+       ".features .copy-paste-icon {\n",
+       "  background-size: 1em 1em;\n",
+       "  width: 1em;\n",
+       "  height: 1em;\n",
+       "  filter: grayscale(100%) opacity(60%);\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  width: 100%;\n",
+       "  margin: 0.01em;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(odd) {\n",
+       "  background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(even) {\n",
+       "  background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:hover {\n",
+       "  background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  table-layout: inherit;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table td {\n",
+       "  text-align: left;\n",
+       "  padding: 0 0.5em;\n",
+       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "  white-space: nowrap;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".total_features {\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  margin-top: 0.5em;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42)</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 sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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/1.9/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=\"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",
+       "            style=\"anchor-name: --doc-link-random_state;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/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\"\n",
+       "            style=\"position-anchor: --doc-link-random_state;\">\n",
+       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</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",
+       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
+       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-penalty;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/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\"\n",
+       "            style=\"position-anchor: --doc-link-penalty;\">\n",
+       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: 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 &#x27;multinomial&#x27; + 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` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-C;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-C;\">\n",
+       "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+       "            l1_ratio\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
+       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 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",
+       "            style=\"anchor-name: --doc-link-dual;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-dual;\">\n",
+       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; 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",
+       "            style=\"anchor-name: --doc-link-tol;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-tol;\">\n",
+       "            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",
+       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
+       "            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",
+       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
+       "            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 &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/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\"\n",
+       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
+       "            class_weight: dict or &#x27;balanced&#x27;, 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 &quot;balanced&quot; 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=&#x27;balanced&#x27;*</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('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-solver;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/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\"\n",
+       "            style=\"position-anchor: --doc-link-solver;\">\n",
+       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `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, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; 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 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; 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 &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<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 &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; 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=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-verbose;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-verbose;\">\n",
+       "            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",
+       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
+       "            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 &lt;warm_start&gt;`.<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",
+       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
+       "            n_jobs: int, default=None<br><br>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",
+       "    \n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Fitted attributes</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                    <tbody>\n",
+       "                        <tr>\n",
+       "                        <th>Name</th>\n",
+       "                        <th>Type</th>\n",
+       "                        <th>Value</th>\n",
+       "                        </tr>\n",
+       "                        \n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-classes_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
+       "            classes_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-classes_;\">\n",
+       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[bool](2,)</td>\n",
+       "           <td>[False, True]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-coef_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
+       "            coef_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-coef_;\">\n",
+       "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[float64](1, 8)</td>\n",
+       "           <td>[[-0.09,-0.84, 0.74,..., 2.01, 1.19,-0.13]]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
+       "            intercept_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
+       "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
+       "           <td>[-1.15]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
+       "            n_features_in_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
+       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">int</td>\n",
+       "           <td>8</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
+       "            n_iter_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
+       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
+       "           <td>[12]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "                    </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
+       " SPDX-License-Identifier: BSD-3-Clause\n",
+       "*/\n",
+       "\n",
+       "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",
+       "\n",
+       "    const parent = element.parentElement;\n",
+       "    if (!parent || !parent.nextElementSibling) {\n",
+       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "/**\n",
+       " * Copy the list of feature names formatted as a Python list.\n",
+       " *\n",
+       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
+       " *   contain a `details` element and a table containing feature named.\n",
+       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
+       " */\n",
+       "function copyFeatureNamesToClipboard(element) {\n",
+       "    var detailsElem = element.closest('.features').querySelector('details');\n",
+       "    var wasOpen = detailsElem.open;\n",
+       "    detailsElem.open = true;\n",
+       "    var content = element.closest('.features').querySelector('tbody')\n",
+       "                  .innerText.trim();\n",
+       "    if (!wasOpen) detailsElem.open = false;\n",
+       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
+       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
+       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
+       "    const originalStyle = element.style;\n",
+       "    const copyMark = document.createElement('span');\n",
+       "    copyMark.innerHTML = '✔';\n",
+       "    copyMark.style.color = 'blue';\n",
+       "    copyMark.style.fontSize = '1em';\n",
+       "\n",
+       "    navigator.clipboard.writeText(formattedText)\n",
+       "        .then(() => {\n",
+       "            element.style.display = 'none';\n",
+       "            element.parentElement.appendChild(copyMark);\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                copyMark.remove();\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'orange';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        });\n",
+       "    return false;\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-1');</script></body>"
+      ],
+      "text/plain": [
+       "LogisticRegression(max_iter=1000, random_state=42)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LogisticRegression(\n",
+    "    max_iter=1000,\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "model.fit(\n",
+    "    X_train_selected,\n",
+    "    y_train\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Evaluate your model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline Accuracy: 0.7838\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(\n",
+    "    X_test_selected\n",
+    ")\n",
+    "\n",
+    "baseline_accuracy = accuracy_score(\n",
+    "    y_test,\n",
+    "    y_pred\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"Baseline Accuracy:\",\n",
+    "    round(baseline_accuracy, 4)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Grid/Random Search**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this lab we will use Grid Search."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Define hyperparameters to fine tune."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'C': [0.01, 0.1, 1, 10, 100], 'solver': ['liblinear', 'lbfgs']}"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid = {\n",
+    "    \"C\": [0.01, 0.1, 1, 10, 100],\n",
+    "    \"solver\": [\"liblinear\", \"lbfgs\"]\n",
+    "}\n",
+    "\n",
+    "param_grid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Run Grid Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters: {'C': 100, 'solver': 'liblinear'}\n",
+      "Best CV accuracy: 0.786\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid_search = GridSearchCV(\n",
+    "    estimator=LogisticRegression(\n",
+    "        max_iter=1000,\n",
+    "        random_state=42\n",
+    "    ),\n",
+    "    param_grid=param_grid,\n",
+    "    cv=5,\n",
+    "    scoring=\"accuracy\",\n",
+    "    n_jobs=-1\n",
+    ")\n",
+    "\n",
+    "grid_search.fit(\n",
+    "    X_train_selected,\n",
+    "    y_train\n",
+    ")\n",
+    "\n",
+    "print(\"Best parameters:\", grid_search.best_params_)\n",
+    "print(\"Best CV accuracy:\", round(grid_search.best_score_, 4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Evaluate your model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline Accuracy: 0.7838\n",
+      "Tuned Accuracy: 0.7844\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_model = grid_search.best_estimator_\n",
+    "\n",
+    "y_pred_tuned = best_model.predict(\n",
+    "    X_test_selected\n",
+    ")\n",
+    "\n",
+    "tuned_accuracy = accuracy_score(\n",
+    "    y_test,\n",
+    "    y_pred_tuned\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"Baseline Accuracy:\",\n",
+    "    round(baseline_accuracy, 4)\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"Tuned Accuracy:\",\n",
+    "    round(tuned_accuracy, 4)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Evaluation\n",
+    "\n",
+    "Grid Search identified the best Logistic Regression hyperparameters as `C = 100` and `solver = 'liblinear'`.\n",
+    "\n",
+    "The baseline model achieved an accuracy of 0.7838, while the tuned model achieved an accuracy of 0.7844.\n",
+    "\n",
+    "Hyperparameter tuning therefore produced a small improvement of 0.0006, equivalent to 0.06 percentage points. This indicates that the baseline Logistic Regression model was already performing close to its optimal configuration for the selected features.\n",
+    "\n",
+    "Although the improvement is small, Grid Search provides a systematic method for selecting hyperparameters using cross validation rather than relying on manually chosen settings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
@@ -321,9 +2305,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python (lab-hyperparameter-tuning)",
    "language": "python",
-   "name": "python3"
+   "name": "lab-hyperparameter-tuning"
   },
   "language_info": {
    "codemirror_mode": {
@@ -335,9 +2319,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.13.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }