diff --git "a/src/SpaceshipTitanicClassification.ipynb" "b/src/SpaceshipTitanicClassification.ipynb"
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@@ -0,0 +1,6340 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e1ea5a5d-5260-4176-9c8f-0a10fb034334",
+   "metadata": {},
+   "source": [
+    "# Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42102af6-b6d9-4155-b68b-537767456b00",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://media.istockphoto.com/id/1195199589/photo/dark-blue-spaceship-futuristic-interior-with-window-view-on-planet-earth-3d-rendering.jpg?s=612x612&w=0&k=20&c=H0_qt9Ry98ksk32RKqUBXlyWbnXtGlAt9ulqzq_UWl8=\" width=800>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eba8731-e7d4-481d-b7ea-9b2baebf3591",
+   "metadata": {},
+   "source": [
+    "### Dataset Download  \n",
+    "You can download the CSV file here:  \n",
+    "[https://www.kaggle.com/competitions/spaceship-titanic/data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ab056fd-efb8-4d87-b2ec-69cb6d7f6bb5",
+   "metadata": {},
+   "source": [
+    "### Introduction\n",
+    "This project focuses on predicting whether a passenger was transported to another dimension in the Spaceship Titanic incident.\n",
+    "The dataset contains passenger information such as age, spending behavior, cabin details, and travel conditions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f522da9-52b7-44b9-80b6-7aa87b91680b",
+   "metadata": {},
+   "source": [
+    "### Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ff26d318-275c-4f16-8439-58757bab5c71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.naive_bayes import BernoulliNB\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "import joblib\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "pd.set_option('display.max_columns',100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fc1e71c-5f5d-4d86-ab04-fc01e6ef4584",
+   "metadata": {},
+   "source": [
+    "### Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "29bbe13d-e1e7-4efe-92c0-70040358f59f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train=pd.read_csv('train.csv')\n",
+    "test=pd.read_csv('test.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17dbf90a-85ab-4e6f-af08-2e92f16ca567",
+   "metadata": {},
+   "source": [
+    "### EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a1ac0c0c-4e83-42bc-a296-4bfde45e36b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>HomePlanet</th>\n",
+       "      <th>CryoSleep</th>\n",
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+       "      <th>Name</th>\n",
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+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <th>0</th>\n",
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+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>B/0/P</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>39.0</td>\n",
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+       "      <td>True</td>\n",
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+       "    <tr>\n",
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+       "      <td>Europa</td>\n",
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+       "      <td>58.0</td>\n",
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+       "      <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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "975323c5-48a6-460c-beb3-398c3993d7a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <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",
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+       "      <th>Name</th>\n",
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+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8688</th>\n",
+       "      <td>9276_01</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>A/98/P</td>\n",
+       "      <td>55 Cancri e</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6819.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1643.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>Gravior Noxnuther</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8689</th>\n",
+       "      <td>9278_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>True</td>\n",
+       "      <td>G/1499/S</td>\n",
+       "      <td>PSO J318.5-22</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>Kurta Mondalley</td>\n",
+       "      <td>False</td>\n",
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+       "    <tr>\n",
+       "      <th>8690</th>\n",
+       "      <td>9279_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>G/1500/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>False</td>\n",
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+       "      <td>1872.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Fayey Connon</td>\n",
+       "      <td>True</td>\n",
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+       "      <th>8691</th>\n",
+       "      <td>9280_01</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>E/608/S</td>\n",
+       "      <td>55 Cancri e</td>\n",
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+       "      <td>False</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>3235.0</td>\n",
+       "      <td>Celeon Hontichre</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8692</th>\n",
+       "      <td>9280_02</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>E/608/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>4688.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>Propsh Hontichre</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n",
+       "8688     9276_01     Europa     False    A/98/P    55 Cancri e  41.0   True   \n",
+       "8689     9278_01      Earth      True  G/1499/S  PSO J318.5-22  18.0  False   \n",
+       "8690     9279_01      Earth     False  G/1500/S    TRAPPIST-1e  26.0  False   \n",
+       "8691     9280_01     Europa     False   E/608/S    55 Cancri e  32.0  False   \n",
+       "8692     9280_02     Europa     False   E/608/S    TRAPPIST-1e  44.0  False   \n",
+       "\n",
+       "      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n",
+       "8688          0.0     6819.0           0.0  1643.0    74.0  Gravior Noxnuther   \n",
+       "8689          0.0        0.0           0.0     0.0     0.0    Kurta Mondalley   \n",
+       "8690          0.0        0.0        1872.0     1.0     0.0       Fayey Connon   \n",
+       "8691          0.0     1049.0           0.0   353.0  3235.0   Celeon Hontichre   \n",
+       "8692        126.0     4688.0           0.0     0.0    12.0   Propsh Hontichre   \n",
+       "\n",
+       "      Transported  \n",
+       "8688        False  \n",
+       "8689        False  \n",
+       "8690         True  \n",
+       "8691        False  \n",
+       "8692         True  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f1805039-70bb-421b-93dd-ff08acccfd3d",
+   "metadata": {},
+   "outputs": [
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+       "      <th>2</th>\n",
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+       "      <td>Europa</td>\n",
+       "      <td>True</td>\n",
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+       "      <td>0021_01</td>\n",
+       "      <td>Europa</td>\n",
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+       "      <td>TRAPPIST-1e</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>585.0</td>\n",
+       "      <td>Meratz Caltilter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0023_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>F/5/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>635.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Brence Harperez</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n",
+       "0     0013_01      Earth      True  G/3/S  TRAPPIST-1e  27.0  False   \n",
+       "1     0018_01      Earth     False  F/4/S  TRAPPIST-1e  19.0  False   \n",
+       "2     0019_01     Europa      True  C/0/S  55 Cancri e  31.0  False   \n",
+       "3     0021_01     Europa     False  C/1/S  TRAPPIST-1e  38.0  False   \n",
+       "4     0023_01      Earth     False  F/5/S  TRAPPIST-1e  20.0  False   \n",
+       "\n",
+       "   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck              Name  \n",
+       "0          0.0        0.0           0.0     0.0     0.0   Nelly Carsoning  \n",
+       "1          0.0        9.0           0.0  2823.0     0.0    Lerome Peckers  \n",
+       "2          0.0        0.0           0.0     0.0     0.0   Sabih Unhearfus  \n",
+       "3          0.0     6652.0           0.0   181.0   585.0  Meratz Caltilter  \n",
+       "4         10.0        0.0         635.0     0.0     0.0   Brence Harperez  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "48340fca-b820-414b-99b7-4978b827d741",
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4272</th>\n",
+       "      <td>9266_02</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>True</td>\n",
+       "      <td>G/1496/S</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>34.0</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>Jeron Peter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4273</th>\n",
+       "      <td>9269_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>TRAPPIST-1e</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>847.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>144.0</td>\n",
+       "      <td>Matty Scheron</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4274</th>\n",
+       "      <td>9271_01</td>\n",
+       "      <td>Mars</td>\n",
+       "      <td>True</td>\n",
+       "      <td>D/296/P</td>\n",
+       "      <td>55 Cancri e</td>\n",
+       "      <td>NaN</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>Jayrin Pore</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4275</th>\n",
+       "      <td>9273_01</td>\n",
+       "      <td>Europa</td>\n",
+       "      <td>False</td>\n",
+       "      <td>D/297/P</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2680.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>523.0</td>\n",
+       "      <td>Kitakan Conale</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4276</th>\n",
+       "      <td>9277_01</td>\n",
+       "      <td>Earth</td>\n",
+       "      <td>True</td>\n",
+       "      <td>G/1498/S</td>\n",
+       "      <td>PSO J318.5-22</td>\n",
+       "      <td>43.0</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>Lilace Leonzaley</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId HomePlanet CryoSleep     Cabin    Destination   Age    VIP  \\\n",
+       "4272     9266_02      Earth      True  G/1496/S    TRAPPIST-1e  34.0  False   \n",
+       "4273     9269_01      Earth     False       NaN    TRAPPIST-1e  42.0  False   \n",
+       "4274     9271_01       Mars      True   D/296/P    55 Cancri e   NaN  False   \n",
+       "4275     9273_01     Europa     False   D/297/P            NaN   NaN  False   \n",
+       "4276     9277_01      Earth      True  G/1498/S  PSO J318.5-22  43.0  False   \n",
+       "\n",
+       "      RoomService  FoodCourt  ShoppingMall   Spa  VRDeck              Name  \n",
+       "4272          0.0        0.0           0.0   0.0     0.0       Jeron Peter  \n",
+       "4273          0.0      847.0          17.0  10.0   144.0     Matty Scheron  \n",
+       "4274          0.0        0.0           0.0   0.0     0.0       Jayrin Pore  \n",
+       "4275          0.0     2680.0           0.0   0.0   523.0    Kitakan Conale  \n",
+       "4276          0.0        0.0           0.0   0.0     0.0  Lilace Leonzaley  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "45590f43-1834-4732-9cc4-0fac3c16b58e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8693, 14)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c371daa1-abbc-47fe-bea5-f5d3f5ab45a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4277, 13)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "75c34a54-da0c-4274-bc3f-e988df6c8399",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.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   object \n",
+      " 1   HomePlanet    8492 non-null   object \n",
+      " 2   CryoSleep     8476 non-null   object \n",
+      " 3   Cabin         8494 non-null   object \n",
+      " 4   Destination   8511 non-null   object \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   object \n",
+      " 13  Transported   8693 non-null   bool   \n",
+      "dtypes: bool(1), float64(6), object(7)\n",
+      "memory usage: 891.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "869be379-27c9-444c-b872-d8c2361171eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4277 entries, 0 to 4276\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   PassengerId   4277 non-null   object \n",
+      " 1   HomePlanet    4190 non-null   object \n",
+      " 2   CryoSleep     4184 non-null   object \n",
+      " 3   Cabin         4177 non-null   object \n",
+      " 4   Destination   4185 non-null   object \n",
+      " 5   Age           4186 non-null   float64\n",
+      " 6   VIP           4184 non-null   object \n",
+      " 7   RoomService   4195 non-null   float64\n",
+      " 8   FoodCourt     4171 non-null   float64\n",
+      " 9   ShoppingMall  4179 non-null   float64\n",
+      " 10  Spa           4176 non-null   float64\n",
+      " 11  VRDeck        4197 non-null   float64\n",
+      " 12  Name          4183 non-null   object \n",
+      "dtypes: float64(6), object(7)\n",
+      "memory usage: 434.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fee2e08b-5b86-4755-9c41-8287510c614b",
+   "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": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "03f1998e-e390-43a3-ad5e-30d119fb93ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId       0\n",
+       "HomePlanet       87\n",
+       "CryoSleep        93\n",
+       "Cabin           100\n",
+       "Destination      92\n",
+       "Age              91\n",
+       "VIP              93\n",
+       "RoomService      82\n",
+       "FoodCourt       106\n",
+       "ShoppingMall     98\n",
+       "Spa             101\n",
+       "VRDeck           80\n",
+       "Name             94\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e073f4e5-c005-45f4-9a75-e00b589cff16",
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>8514.000000</td>\n",
+       "      <td>8512.000000</td>\n",
+       "      <td>8510.000000</td>\n",
+       "      <td>8485.000000</td>\n",
+       "      <td>8510.000000</td>\n",
+       "      <td>8505.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>28.827930</td>\n",
+       "      <td>224.687617</td>\n",
+       "      <td>458.077203</td>\n",
+       "      <td>173.729169</td>\n",
+       "      <td>311.138778</td>\n",
+       "      <td>304.854791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>14.489021</td>\n",
+       "      <td>666.717663</td>\n",
+       "      <td>1611.489240</td>\n",
+       "      <td>604.696458</td>\n",
+       "      <td>1136.705535</td>\n",
+       "      <td>1145.717189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>46.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>14327.000000</td>\n",
+       "      <td>29813.000000</td>\n",
+       "      <td>23492.000000</td>\n",
+       "      <td>22408.000000</td>\n",
+       "      <td>24133.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Age   RoomService     FoodCourt  ShoppingMall           Spa  \\\n",
+       "count  8514.000000   8512.000000   8510.000000   8485.000000   8510.000000   \n",
+       "mean     28.827930    224.687617    458.077203    173.729169    311.138778   \n",
+       "std      14.489021    666.717663   1611.489240    604.696458   1136.705535   \n",
+       "min       0.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "25%      19.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "50%      27.000000      0.000000      0.000000      0.000000      0.000000   \n",
+       "75%      38.000000     47.000000     76.000000     27.000000     59.000000   \n",
+       "max      79.000000  14327.000000  29813.000000  23492.000000  22408.000000   \n",
+       "\n",
+       "             VRDeck  \n",
+       "count   8505.000000  \n",
+       "mean     304.854791  \n",
+       "std     1145.717189  \n",
+       "min        0.000000  \n",
+       "25%        0.000000  \n",
+       "50%        0.000000  \n",
+       "75%       46.000000  \n",
+       "max    24133.000000  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "329d12f0-4210-4695-a424-171424c3df6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(train.corr(numeric_only=True), annot=True, cmap='tab10')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "305edbf0-c691-4faa-8545-75c188c378c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HomePlanet\n",
+       "Earth     4602\n",
+       "Europa    2131\n",
+       "Mars      1759\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['HomePlanet'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0a7fa308-3b91-40e1-9833-cf85c19b35be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HomePlanet\n",
+       "Earth     2263\n",
+       "Europa    1002\n",
+       "Mars       925\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['HomePlanet'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a8e60559-cf74-4401-b5b9-25cb770c5446",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CryoSleep\n",
+       "False    5439\n",
+       "True     3037\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['CryoSleep'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2332d163-e235-4e53-bae4-0c6748b25060",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CryoSleep\n",
+       "False    2640\n",
+       "True     1544\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['CryoSleep'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0b8b3c21-ddc7-4585-8896-8f1defa6d4d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Cabin\n",
+       "G/734/S     8\n",
+       "F/1194/P    7\n",
+       "B/201/P     7\n",
+       "G/981/S     7\n",
+       "G/109/P     7\n",
+       "           ..\n",
+       "E/56/P      1\n",
+       "A/98/P      1\n",
+       "G/1499/S    1\n",
+       "G/1500/S    1\n",
+       "D/252/P     1\n",
+       "Name: count, Length: 6560, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['Cabin'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e4898b37-cf36-46a8-a849-b1cf395a89c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Cabin\n",
+       "G/160/P    8\n",
+       "B/31/P     7\n",
+       "G/748/S    7\n",
+       "E/228/S    7\n",
+       "D/273/S    7\n",
+       "          ..\n",
+       "G/4/P      1\n",
+       "F/183/P    1\n",
+       "D/33/S     1\n",
+       "G/5/S      1\n",
+       "F/4/S      1\n",
+       "Name: count, Length: 3265, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['Cabin'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "65fe6d8e-6e26-4a43-9fd1-0cb297de4f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Destination\n",
+       "TRAPPIST-1e      5915\n",
+       "55 Cancri e      1800\n",
+       "PSO J318.5-22     796\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['Destination'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7f153971-edaa-484a-930b-52f2e67eb2d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Destination\n",
+       "TRAPPIST-1e      2956\n",
+       "55 Cancri e       841\n",
+       "PSO J318.5-22     388\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['Destination'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b9966c5c-6b34-4d0c-a7f0-46ba49a5723c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VIP\n",
+       "False    8291\n",
+       "True      199\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['VIP'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "66046600-36bd-4412-93f8-82c7046a3e26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VIP\n",
+       "False    4110\n",
+       "True       74\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['VIP'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fba076b7-6745-4ce1-80ea-91fa06c3c2b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RoomService\n",
+       "0.0       5577\n",
+       "1.0        117\n",
+       "2.0         79\n",
+       "3.0         61\n",
+       "4.0         47\n",
+       "          ... \n",
+       "6899.0       1\n",
+       "1954.0       1\n",
+       "3146.0       1\n",
+       "1297.0       1\n",
+       "2555.0       1\n",
+       "Name: count, Length: 1273, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['RoomService'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c21efc88-0d44-44ef-ab6f-4c31ee07a780",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RoomService\n",
+       "0.0       2726\n",
+       "1.0         68\n",
+       "2.0         34\n",
+       "3.0         28\n",
+       "4.0         24\n",
+       "          ... \n",
+       "2351.0       1\n",
+       "5454.0       1\n",
+       "773.0        1\n",
+       "3966.0       1\n",
+       "598.0        1\n",
+       "Name: count, Length: 842, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['RoomService'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a5ee7610-d45a-4dcb-bc49-8eba89fd54dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FoodCourt\n",
+       "0.0        5456\n",
+       "1.0         116\n",
+       "2.0          75\n",
+       "3.0          53\n",
+       "4.0          53\n",
+       "           ... \n",
+       "1585.0        1\n",
+       "749.0         1\n",
+       "1961.0        1\n",
+       "522.0         1\n",
+       "14773.0       1\n",
+       "Name: count, Length: 1507, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['FoodCourt'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9166b384-244a-4b91-b890-731dd6f09dd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FoodCourt\n",
+       "0.0       2690\n",
+       "1.0         59\n",
+       "2.0         30\n",
+       "4.0         22\n",
+       "3.0         21\n",
+       "          ... \n",
+       "6452.0       1\n",
+       "6977.0       1\n",
+       "878.0        1\n",
+       "1925.0       1\n",
+       "7708.0       1\n",
+       "Name: count, Length: 902, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['FoodCourt'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b5f6e754-2f43-4ef2-acea-7e8fc0f8e7d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ShoppingMall\n",
+       "0.0        5587\n",
+       "1.0         153\n",
+       "2.0          80\n",
+       "3.0          59\n",
+       "4.0          45\n",
+       "           ... \n",
+       "563.0         1\n",
+       "23492.0       1\n",
+       "816.0         1\n",
+       "1970.0        1\n",
+       "204.0         1\n",
+       "Name: count, Length: 1115, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['ShoppingMall'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "61b6baa3-b80d-4080-9bbd-964bc7179ec1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ShoppingMall\n",
+       "0.0       2744\n",
+       "1.0         72\n",
+       "3.0         35\n",
+       "2.0         32\n",
+       "4.0         24\n",
+       "          ... \n",
+       "268.0        1\n",
+       "1464.0       1\n",
+       "1319.0       1\n",
+       "2257.0       1\n",
+       "621.0        1\n",
+       "Name: count, Length: 715, dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['ShoppingMall'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f915dfac-7482-4aac-9e60-43f7ab0e1c13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Spa\n",
+       "0.0       5324\n",
+       "1.0        146\n",
+       "2.0        105\n",
+       "5.0         53\n",
+       "3.0         53\n",
+       "          ... \n",
+       "780.0        1\n",
+       "800.0        1\n",
+       "1239.0       1\n",
+       "3761.0       1\n",
+       "5025.0       1\n",
+       "Name: count, Length: 1327, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['Spa'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "76704689-4b3a-4414-adbd-b3205becaf1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Spa\n",
+       "0.0       2611\n",
+       "1.0         72\n",
+       "2.0         43\n",
+       "3.0         29\n",
+       "4.0         27\n",
+       "          ... \n",
+       "78.0         1\n",
+       "1716.0       1\n",
+       "1377.0       1\n",
+       "1812.0       1\n",
+       "2412.0       1\n",
+       "Name: count, Length: 833, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['Spa'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "d7f3ad51-a284-4af8-b91e-7528cd994b6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VRDeck\n",
+       "0.0       5495\n",
+       "1.0        139\n",
+       "2.0         70\n",
+       "3.0         56\n",
+       "5.0         51\n",
+       "          ... \n",
+       "471.0        1\n",
+       "2064.0       1\n",
+       "802.0        1\n",
+       "1556.0       1\n",
+       "5057.0       1\n",
+       "Name: count, Length: 1306, dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['VRDeck'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "5da4ad23-3e26-4310-80f2-4e3caa3eec5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VRDeck\n",
+       "0.0       2757\n",
+       "1.0         72\n",
+       "2.0         38\n",
+       "3.0         33\n",
+       "7.0         23\n",
+       "          ... \n",
+       "3523.0       1\n",
+       "1172.0       1\n",
+       "2628.0       1\n",
+       "4617.0       1\n",
+       "1995.0       1\n",
+       "Name: count, Length: 796, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['VRDeck'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6975b33d-397c-4e53-b748-b181dacbec48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=train, x='Transported')\n",
+    "plt.title('Target Distribution (Transported)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9111301a-e4c4-47b5-b355-26497898791e",
+   "metadata": {},
+   "source": [
+    "The target classes are almost balanced, with a similar number of transported and non-transported passengers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "22fcd8cb-2c8c-4f15-a6d2-963f1f8ec531",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=train, x='HomePlanet', hue='Transported')\n",
+    "plt.title('HomePlanet vs Transported')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98deac32-307d-4dc6-825e-a684ba476f21",
+   "metadata": {},
+   "source": [
+    "Passengers from Europa are transported more often than passengers from Earth and Mars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a274e8dc-7ca1-4551-9730-c084dbef7255",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=train, x='CryoSleep', hue='Transported')\n",
+    "plt.title('CryoSleep vs Transported')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6e17e25-7f38-4915-9f70-126b23f26e15",
+   "metadata": {},
+   "source": [
+    "Passengers in cryosleep are much more likely to be transported than those not in cryosleep."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "29f36ae8-475d-475c-9c8b-ed6b64643ff7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data=train, x='Age', hue='Transported', bins=30, kde=True)\n",
+    "plt.title('Age Distribution by Transported')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0aa6e22-04de-4001-98ab-0d2ac6e5a749",
+   "metadata": {},
+   "source": [
+    "The age distributions are very similar, so age alone does not strongly affect transportation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "66e37a2d-0e6a-4a1b-802c-4700824340d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Feature Engineering\n",
+    "total_spend=['RoomService','FoodCourt','ShoppingMall','Spa','VRDeck']\n",
+    "train['TotalSpend']=train[total_spend].sum(axis=1)\n",
+    "test['TotalSpend']=test[total_spend].sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46fd6128-2400-47dc-8f56-f0f44bbaca62",
+   "metadata": {},
+   "source": [
+    "The TotalSpend feature represents the total amount of money a passenger spent on all onboard services."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "4eec55e1-2305-44cd-aea5-ec07527f8a55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(data=train, x='Transported', y='TotalSpend')\n",
+    "plt.title('TotalSpend vs Transported')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8353c42-cd23-4b57-8e5b-3707f07ccc62",
+   "metadata": {},
+   "source": [
+    "Transported passengers usually spend much less money than non-transported passengers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "b2a2d563-a171-479f-8487-f49330f357db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=train, x='VIP', hue='Transported')\n",
+    "plt.title('VIP vs Transported')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19ddf57a-bad5-42bb-82a8-26478cc8f4fb",
+   "metadata": {},
+   "source": [
+    "VIP status has only a small effect because most passengers are not VIP and show similar outcomes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f80b1d0-8269-4f9a-ba99-561bbb9f37c5",
+   "metadata": {},
+   "source": [
+    "### Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "9beebe24-62e5-4c7b-926c-ffcc4b7ecc38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols=['HomePlanet','Destination','Cabin','CryoSleep','VIP']\n",
+    "\n",
+    "for col in cols:\n",
+    "    train[col]=train[col].fillna('Unknown')\n",
+    "    test[col]=test[col].fillna('Unknown')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bd9c814-3d42-4570-9454-267a64814e28",
+   "metadata": {},
+   "source": [
+    "Missing values in categorical features are replaced with an Unknown category so the model can handle missing information consistently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "c9b9a7ff-9960-4c7e-ad71-7a94b428f217",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train[['Deck','CabinNum','Side']]=train['Cabin'].str.split('/', expand=True)\n",
+    "test[['Deck','CabinNum','Side']]=test['Cabin'].str.split('/', expand=True)\n",
+    "\n",
+    "train['Deck']=train['Deck'].fillna('Unknown')\n",
+    "train['Side']=train['Side'].fillna('Unknown')\n",
+    "\n",
+    "test['Deck']=test['Deck'].fillna('Unknown')\n",
+    "test['Side']=test['Side'].fillna('Unknown')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ef90c63-25f5-4065-8e44-1d07c4955b33",
+   "metadata": {},
+   "source": [
+    "The cabin column is split into separate features to extract useful location information about the passenger’s cabin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "329ae7bd-65e5-49eb-a865-1357ffbf2ada",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train['NotSpend']=(train['TotalSpend'] == 0).astype(int)\n",
+    "test['NotSpend']=(test['TotalSpend'] == 0).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4daf005d-52aa-43d9-9e0c-c10096893762",
+   "metadata": {},
+   "source": [
+    "This feature shows whether a passenger spent no money at all, which is strongly related to being transported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "031e2d66-e0be-429a-bc50-eecf8e280ca0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train['Group']=train['PassengerId'].str.split('_').str[0]\n",
+    "test['Group']=test['PassengerId'].str.split('_').str[0]\n",
+    "\n",
+    "train['GroupSize']=train.groupby('Group')['Group'].transform('count')\n",
+    "test['GroupSize']=test.groupby('Group')['Group'].transform('count')\n",
+    "\n",
+    "train['IsAlone']=(train['GroupSize'] == 1).astype(int)\n",
+    "test['IsAlone']=(test['GroupSize'] == 1).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f04e0543-181d-42d8-806e-a2efe9518869",
+   "metadata": {},
+   "source": [
+    "These features describe whether a passenger travels alone or in a group, which helps the model capture group-related behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "7846f938-b72b-4358-b9ac-1220b78da7f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "passengerid=test['PassengerId'].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "fc1061b3-4d64-4390-96ce-e36734e7dcf3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drop=['Name','Cabin','CabinNum','PassengerId','Group']\n",
+    "\n",
+    "train=train.drop(columns=drop)\n",
+    "test=test.drop(columns=drop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "6934aab5-7150-4dc5-a470-fb67633e80c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HomePlanet        0\n",
+       "CryoSleep         0\n",
+       "Destination       0\n",
+       "Age             179\n",
+       "VIP               0\n",
+       "RoomService     181\n",
+       "FoodCourt       183\n",
+       "ShoppingMall    208\n",
+       "Spa             183\n",
+       "VRDeck          188\n",
+       "Transported       0\n",
+       "TotalSpend        0\n",
+       "Deck              0\n",
+       "Side              0\n",
+       "NotSpend          0\n",
+       "GroupSize         0\n",
+       "IsAlone           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "ef75cf06-2acc-40a3-8d2d-1ea15132b4cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HomePlanet        0\n",
+       "CryoSleep         0\n",
+       "Destination       0\n",
+       "Age              91\n",
+       "VIP               0\n",
+       "RoomService      82\n",
+       "FoodCourt       106\n",
+       "ShoppingMall     98\n",
+       "Spa             101\n",
+       "VRDeck           80\n",
+       "TotalSpend        0\n",
+       "Deck              0\n",
+       "Side              0\n",
+       "NotSpend          0\n",
+       "GroupSize         0\n",
+       "IsAlone           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "31323060-9729-4dfb-a670-5bb650cf3a7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d={False: 0, True: 1,'Unknown': -1}\n",
+    "\n",
+    "train['CryoSleep']=train['CryoSleep'].map(d).fillna(-1).astype(int)\n",
+    "test['CryoSleep']=test['CryoSleep'].map(d).fillna(-1).astype(int)\n",
+    "\n",
+    "train['VIP']=train['VIP'].map(d).fillna(-1).astype(int)\n",
+    "test['VIP']=test['VIP'].map(d).fillna(-1).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "085a464a-e24b-4505-8cf5-c1001c1b3713",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HomePlanet        0\n",
+       "CryoSleep         0\n",
+       "Destination       0\n",
+       "Age             179\n",
+       "VIP               0\n",
+       "RoomService     181\n",
+       "FoodCourt       183\n",
+       "ShoppingMall    208\n",
+       "Spa             183\n",
+       "VRDeck          188\n",
+       "Transported       0\n",
+       "TotalSpend        0\n",
+       "Deck              0\n",
+       "Side              0\n",
+       "NotSpend          0\n",
+       "GroupSize         0\n",
+       "IsAlone           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "67ef118b-bb88-4d89-a952-9d3843770a3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HomePlanet        0\n",
+       "CryoSleep         0\n",
+       "Destination       0\n",
+       "Age              91\n",
+       "VIP               0\n",
+       "RoomService      82\n",
+       "FoodCourt       106\n",
+       "ShoppingMall     98\n",
+       "Spa             101\n",
+       "VRDeck           80\n",
+       "TotalSpend        0\n",
+       "Deck              0\n",
+       "Side              0\n",
+       "NotSpend          0\n",
+       "GroupSize         0\n",
+       "IsAlone           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "333063b8-1564-4e8f-a143-b1a7a03a6ca6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols=['Age','RoomService','FoodCourt','ShoppingMall','Spa','VRDeck']\n",
+    "\n",
+    "for col in cols:\n",
+    "    median=train[col].median()\n",
+    "    train[col]=train[col].fillna(median)\n",
+    "    test[col]=test[col].fillna(median)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "9fa377d9-676e-48cb-a63d-cbceb4d53278",
+   "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",
+       "TotalSpend      0\n",
+       "Deck            0\n",
+       "Side            0\n",
+       "NotSpend        0\n",
+       "GroupSize       0\n",
+       "IsAlone         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "ea3577f1-8e8e-4d54-bf8a-e9c23ae29345",
+   "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",
+       "TotalSpend      0\n",
+       "Deck            0\n",
+       "Side            0\n",
+       "NotSpend        0\n",
+       "GroupSize       0\n",
+       "IsAlone         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "8d69e758-3fb2-43de-bd5b-5f9a86ce00d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 8693 entries, 0 to 8692\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   HomePlanet    8693 non-null   object \n",
+      " 1   CryoSleep     8693 non-null   int64  \n",
+      " 2   Destination   8693 non-null   object \n",
+      " 3   Age           8693 non-null   float64\n",
+      " 4   VIP           8693 non-null   int64  \n",
+      " 5   RoomService   8693 non-null   float64\n",
+      " 6   FoodCourt     8693 non-null   float64\n",
+      " 7   ShoppingMall  8693 non-null   float64\n",
+      " 8   Spa           8693 non-null   float64\n",
+      " 9   VRDeck        8693 non-null   float64\n",
+      " 10  Transported   8693 non-null   bool   \n",
+      " 11  TotalSpend    8693 non-null   float64\n",
+      " 12  Deck          8693 non-null   object \n",
+      " 13  Side          8693 non-null   object \n",
+      " 14  NotSpend      8693 non-null   int64  \n",
+      " 15  GroupSize     8693 non-null   int64  \n",
+      " 16  IsAlone       8693 non-null   int64  \n",
+      "dtypes: bool(1), float64(7), int64(5), object(4)\n",
+      "memory usage: 1.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73b3d7f7-b5f3-461b-8c81-507ba2fbf135",
+   "metadata": {},
+   "source": [
+    "### Modelling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "d9adbd67-6af4-4dad-9b10-6a1f0d960ee9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=train.drop('Transported', axis=1)\n",
+    "y=train['Transported'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "eb325c58-fe9e-4413-8552-9990bab5efb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all=pd.concat([x,test],axis=0)\n",
+    "all=pd.get_dummies(all,drop_first=True)\n",
+    "\n",
+    "x= all.iloc[:len(train)]\n",
+    "x_test=all.iloc[len(train):]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "9cb0db7b-46a3-4376-a165-459439d7d57b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train,x_val, y_train, y_val=train_test_split(x,y, test_size=.20, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b3d19271-b4fe-4cd4-a5bc-a404b47a176c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g=GaussianNB()\n",
+    "b=BernoulliNB()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "2e929afc-8858-426b-bd33-3284c476ee74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #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",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\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",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GaussianNB</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('priors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">priors&nbsp;</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('var_smoothing',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">var_smoothing&nbsp;</td>\n",
+       "            <td class=\"value\">1e-09</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "GaussianNB()"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "287772a0-d98b-4b16-8d57-0981f644a461",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gtahmin=g.predict(x_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "3b4056be-918a-4f98-aa2d-dfb04f3912d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.753306497987349"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_val,gtahmin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "7f43a6dd-54c4-4968-9df7-0afa14460716",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm=confusion_matrix(y_val, gtahmin).astype(int)\n",
+    "\n",
+    "sns.heatmap(cm, annot=True, fmt='d')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "298e21d2-36a0-49b0-bd54-afb90a684692",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\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",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 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: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\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",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BernoulliNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>BernoulliNB</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.naive_bayes.BernoulliNB.html\">?<span>Documentation for BernoulliNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">alpha&nbsp;</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('force_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">force_alpha&nbsp;</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('binarize',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">binarize&nbsp;</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('fit_prior',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_prior&nbsp;</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('class_prior',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_prior&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "BernoulliNB()"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "d2dbb791-158e-41d6-9117-78a3cc775517",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "btahmin=b.predict(x_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "22f107e6-68d1-49f2-81f3-598a4e13031a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7400805060379528"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_val,btahmin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "85eadf73-6369-483b-8363-dfbd1579a34c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2WmYNAgAA8IEKAgAAZlQQCAgAAFiw0zItBgAAYEUFAQAAExYpEhAAALAiINBiAAAAVlQQAAAwY5EiAQEAADPWINBiAACg1Zg7d65sNpvX4XA4PNcNw9DcuXOVkJCgsLAwDRo0SDt37vS6h8vl0rRp09S+fXtFRERo5MiRKisra/JcCAgAAJg1+PFool69eqm8vNxzbN++3XOtoKBACxcu1OLFi7VlyxY5HA4NHTpUR48e9YzJzs7WmjVrtHr1am3cuFHHjh3TiBEjVF9f36R50GIAAMAkkC2G4OBgr6rB/zEMQ4sWLdKcOXN04403SpKWL1+uuLg4rVq1SpMmTVJ1dbWeffZZrVixQhkZGZKklStXqnPnztqwYYOGDRvW6HlQQQAAwMyPFQSXy6Wamhqvw+VynfGtd+/erYSEBCUmJurmm2/Wnj17JEl79+6V0+lUZmamZ6zdbtfAgQO1adMmSVJpaanq6uq8xiQkJCg5OdkzprEICAAANKP8/HxFRUV5Hfn5+T7HpqWl6YUXXtC7776rp59+Wk6nU+np6Tp8+LCcTqckKS4uzus1cXFxnmtOp1OhoaGKjo4+45jGosUAAICJ4cfHHHNzc5WTk+N1zm63+xw7fPhwz39PSUlR3759ddFFF2n58uW66qqrJEk2m817roZhOWfWmDFmVBAAADDzY4vBbrerXbt2XseZAoJZRESEUlJStHv3bs+6BHMloKKiwlNVcDgccrvdqqqqOuOYxiIgAADQSrlcLn3xxReKj49XYmKiHA6HiouLPdfdbrdKSkqUnp4uSUpNTVVISIjXmPLycu3YscMzprFoMQAAYOLPFkNTzJw5U9ddd50uuOACVVRUaP78+aqpqdGECRNks9mUnZ2tvLw8JSUlKSkpSXl5eQoPD9fYsWMlSVFRUZo4caJmzJih2NhYxcTEaObMmUpJSfE81dBYBAQAAMwCFBDKyso0ZswYVVZWqkOHDrrqqqu0efNmdenSRZI0a9Ys1dbWasqUKaqqqlJaWprWr1+vyMhIzz0KCwsVHBysrKws1dbWasiQISoqKlJQUFCT5mIzDKNV7CdZV7kn0FMAWp2whP6BngLQKp1yH2jW+1cOG+i3e7V/t8Rv92pJVBAAADAJVIuhNSEgAABgQkAgIAAAYEFA4DFHAADgAxUEAADMjKbtOvjviIAAAIAJLQZaDAAAwAcqCAAAmBgNtBgICAAAmNBioMUAAAB8oIIAAICJwVMMBAQAAMxoMdBiAAAAPlBBAADAhKcYCAgAAFgYRqBnEHgEBAAATKggsAYBAAD4QAUBAAATKggEBAAALFiDQIsBAAD4QAUBAAATWgwEBAAALNhqmRYDAADwgQoCAAAmfBcDAQEAAIsGWgy0GAAAgBUVBAAATFikSEAAAMCCxxwJCAAAWLCTImsQAACAD1QQAAAwocVAQAAAwILHHGkxAAAAH6ggAABgwmOOBAQAACx4ioEWAwAA8IEKAgAAJixSJCAAAGDBGgRaDAAAwAcqCAAAmLBIkYAAAIAFaxBaUUD4TWpOoKcAtDpHnxoX6CkA5yTWILAGAQAA+NBqKggAALQWtBgICAAAWLBGkRYDAADwgQoCAAAmtBgICAAAWPAUAy0GAADgAxUEAABMGgI9gVaAgAAAgIkhWgy0GAAAgAUVBAAATBrYCIGAAACAWQMtBgICAABmrEFgDQIAAPCBCgIAACY85khAAADAghYDLQYAAOADAQEAAJMGPx7/rPz8fNlsNmVnZ3vOGYahuXPnKiEhQWFhYRo0aJB27tzp9TqXy6Vp06apffv2ioiI0MiRI1VWVtbk9ycgAABgEuiAsGXLFi1btkyXXnqp1/mCggItXLhQixcv1pYtW+RwODR06FAdPXrUMyY7O1tr1qzR6tWrtXHjRh07dkwjRoxQfX19k+ZAQAAAoBU5duyYxo0bp6efflrR0dGe84ZhaNGiRZozZ45uvPFGJScna/ny5Tpx4oRWrVolSaqurtazzz6rBQsWKCMjQ71799bKlSu1fft2bdiwoUnzICAAAGBiyOa3w+VyqaamxutwuVxnfO+pU6fq2muvVUZGhtf5vXv3yul0KjMz03PObrdr4MCB2rRpkySptLRUdXV1XmMSEhKUnJzsGdNYBAQAAEwabP478vPzFRUV5XXk5+f7fN/Vq1frb3/7m8/rTqdTkhQXF+d1Pi4uznPN6XQqNDTUq/JgHtNYPOYIAEAzys3NVU5Ojtc5u91uGbd//37dfffdWr9+vc4777wz3s9m834E0zAMyzmzxowxo4IAAIBJg2x+O+x2u9q1a+d1+AoIpaWlqqioUGpqqoKDgxUcHKySkhI98cQTCg4O9lQOzJWAiooKzzWHwyG3262qqqozjmksAgIAACaGH4/GGjJkiLZv365t27Z5jj59+mjcuHHatm2bunbtKofDoeLiYs9r3G63SkpKlJ6eLklKTU1VSEiI15jy8nLt2LHDM6axaDEAAGASiK2WIyMjlZyc7HUuIiJCsbGxnvPZ2dnKy8tTUlKSkpKSlJeXp/DwcI0dO1aSFBUVpYkTJ2rGjBmKjY1VTEyMZs6cqZSUFMuix59CQAAA4Cwxa9Ys1dbWasqUKaqqqlJaWprWr1+vyMhIz5jCwkIFBwcrKytLtbW1GjJkiIqKihQUFNSk97IZhtGUCkizGdNlVKCnALQ6z83tHugpAK1S2G0FzXr/V+PH+e1eN5W/6Ld7tSQqCAAAmLSKv5wDjEWKAADAggoCAAAmgVik2NoQEAAAMGlo2p5C/5ZoMQAAAAsqCAAAmDSIEgIBAQAAE55ioMUAAAB8oIIAAIAJixQJCAAAWPCYIwEBAAAL1iCwBgEAAPhABQEAABPWIBAQAACwYA0CLQYAAOADFQQAAEyoIBAQAACwMFiDQIsBAABYUUEAAMCEFgMBAQAACwICLQYAAOADFQQAAEzYapmAAACABTspEhAAALBgDQJrEAAAgA9UEAAAMKGCQEAAAMCCRYq0GAAAgA9UEAAAMOEpBgICAAAWrEGgxQAAAHygggAAgAmLFAkIAABYNBARaDEAAAArKggAAJiwSJGAAACABQ0GAgIAABZUEFiDAAAAfKCCAACACTspEhAAALDgMUdaDAAAwAcqCAAAmFA/ICAAAGDBUwy0GAAAgA9UEAAAMGGRIgEBAAAL4gEtBgAA4AMVBAAATFikSEAAAMCCNQgEBAAALIgHrEEAAAA+UEEAAMCENQgEBAAALAyaDLQYAACAFRUEAABMaDEQEAAAsOAxR1oMAADAByoIAACYUD8gIJwTrp/yK1159VVKuKiT3Cdd+qp0l156dLnK9xz0jLGHn6cx945Xn8w0RUZH6lBZhdY9/z/asHKdJKl9p476w8fLfN5/0X8U6H/f2dQinwXwl6UffamnPt7ldS42wq4/T7taknT4+Ektev9zbf6mQkdPntIVnWM1e2iKusS09Yx/dds3WruzTF9+V63j7lP6MPsatTsvpEU/B5pHoFoMS5cu1dKlS/XNN99Iknr16qUHHnhAw4cPlyQZhqF58+Zp2bJlqqqqUlpamp588kn16tXLcw+Xy6WZM2fqpZdeUm1trYYMGaIlS5aoU6dOTZoLAeEc0COtl9a/sFZ7Pt2tNsFBGn3POOWumKt7MqbJVeuSJP3mgdvVs2+KnsxepENlFbq0/+W6ff4kVX13RKXFn+jwwUpN7nOr132HjMnUdZNv0LYP/haATwX86y5qH6mnbk73/NymjU3S6X+Ep7/2iYLbtFHhr9LUNjRYK7b8P01evUmv3/FLhYWe/qfzZF29+nXtqH5dO+qJki8C8hnw76VTp0569NFH1a1bN0nS8uXLdf3112vr1q3q1auXCgoKtHDhQhUVFal79+6aP3++hg4dql27dikyMlKSlJ2drbffflurV69WbGysZsyYoREjRqi0tFRBQUGNngtrEM4Bj054SB+++p7Kdu/Xvi++0R9n/kEdOnVUYspFnjFJV1ysD197X19s3qHKsgq999J6ffvFN+p66en/kRoNDao+9L3XceXVV+kv//2xXCdOBuqjAf+SoDY2tW97nueICbdLkvZVHddnB6t037BLlRwfrQtjI3Vf5mU64T6ltV8c8Lz+lisv0u19uyslISZQHwHNpMGPR1Ncd911uuaaa9S9e3d1795djzzyiNq2bavNmzfLMAwtWrRIc+bM0Y033qjk5GQtX75cJ06c0KpVqyRJ1dXVevbZZ7VgwQJlZGSod+/eWrlypbZv364NGzY0aS4EhHNQeGS4JOnY98c853Zt+UKpGVcqOu70P3Q9+yYrPjFBn5Vs9XmPxOSLdGGvrnr/5eLmnzDQTPZVHdfQxet0zdJizX7zryr7/rgkyX3q9D/r9uB//LUV1MamkKA22rr/cEDmipZl+PE/LpdLNTU1XofL5frJOdTX12v16tU6fvy4+vbtq71798rpdCozM9Mzxm63a+DAgdq06XSbt7S0VHV1dV5jEhISlJyc7BnTWASEc9D4/7xdX37yucq+2uc5VzT3GR3YvV9LPnlOK75+Vfcuf1DP3f+Udv3Vd9l08M0ZKtu9X7tLd/m8DrR2KQnRmn/tFVqSla4Hhl+mymMnNWHFR/q+1q0LY9sqvl2Ynij5XDUn3aqrb9Bzf/lKlcddqjxOxexc4M8KQn5+vqKioryO/Pz8M7739u3b1bZtW9ntdk2ePFlr1qxRz5495XQ6JUlxcXFe4+Pi4jzXnE6nQkNDFR0dfcYxjeX3NQj79+/Xgw8+qOeee+6MY1wulyU91Rv1CrI1vjeCf85tD/9WF1xyoebelOt1/urbrlW33hfrv25/RJUHKnRJWi/dPn+Svq84oh0ff+Y1NsQeqvSRA7TmD6+05NQBv/rFRf/4RzZJ7XRZQoxGPLVBb2/fp/E/76YFN/5cc9/ZqgGL1irIZlPahR3Ur2vHAM4YZ6vc3Fzl5OR4nbPb7Wccf/HFF2vbtm36/vvv9dprr2nChAkqKSnxXLfZbF7jDcOwnDNrzBgzvweEI0eOaPny5T8aEPLz8zVv3jyvc73aXayU8y/x93TwA7fOu1OpGT/XvKz7dMT5jzJpiD1UN99zixZOelRb3yuVJO378lt16ZmoEb8dZQkIadekyx4Wqg9fe79F5w80p7DQYHXr0E77qk63GXo6ztcrtw/W0ZN1qmtoUEy4XbcsL1HP+PMDO1G0CH9+F4Pdbv/RQGAWGhrqWaTYp08fbdmyRY8//rhmz54t6XSVID4+3jO+oqLCU1VwOBxyu92qqqryqiJUVFQoPT1dTdHkgPDWW2/96PU9e/b85D18pak7ksc1dSpoglsfulNXDrtKD4++X4f2V3hdCw4JUnBoiBoavH8hGuobZGtj7UINHp2h0g1bdPRITbPOGWhJ7lP12nv4qK7oFOt1PvLvjy1+e+SYPnd+rykDegRiemhhrWmrZcM4vY4hMTFRDodDxcXF6t27tyTJ7XarpKREjz32mCQpNTVVISEhKi4uVlZWliSpvLxcO3bsUEFBQZPet8kBYdSoUbLZbDKMM6ernypj+EpTtBeaz+3zJyl95AAtuDNPtcdrFdXhfEnSiZoTqnO5VXusVp//ZYfG3TdB7pNuVR6oUI+0ZA341SCtePh5r3vFdXHokrSeKrj14QB8EsB/Fr63QwO6ORTfLkxHTrj09Mdf6bjrlK5L6SxJWv/lAUWH2RUfFabdFTUq2LBdg5PilZ74jzZD5bGTqjzu0v6/L278+lCNwkODFd8uTFFhoQH5XDi73XfffRo+fLg6d+6so0ePavXq1frggw+0bt062Ww2ZWdnKy8vT0lJSUpKSlJeXp7Cw8M1duxYSVJUVJQmTpyoGTNmKDY2VjExMZo5c6ZSUlKUkZHRpLk0OSDEx8frySef1KhRo3xe37Ztm1JTU5t6WzSjoeNPb7DxwCuPeJ1fOuMJffjqe5KkJ6b9XjfPGq+7Hp+utue31aGyQ3r5v170bJT0fwZlZajKeUSffbitReYONJfvjp5U7lt/VdUJt6LD7bo0IVov/Ka/EqJOP+VTeeykFvx5hw4fd6lD2/M0IrmzftvvYq97/GnrN16bLd3+4kZJ0rxreuv6Sy9ouQ8Dv2v4kT+Cm9N3332n8ePHq7y8XFFRUbr00ku1bt06DR06VJI0a9Ys1dbWasqUKZ6NktavX+/ZA0GSCgsLFRwcrKysLM9GSUVFRU3aA0GSbMaPlQJ8GDlypC6//HI99NBDPq9/+umn6t27txoamlagGdNlVJPGA+eC5+Z2D/QUgFYp7Lamlcub6pYuN/rtXiu/fd1v92pJTa4g3HPPPTp+/PgZr3fr1k3vv8/iNQAAzmZNDgj9+/f/0esREREaOHDgPz0hAAACja975rsYAACw8OdjjmcrdlIEAAAWVBAAADBpTfsgBAoBAQAAE9YgEBAAALBgDQJrEAAAgA9UEAAAMGENAgEBAACLJm4y/G+JFgMAALCgggAAgAlPMRAQAACwYA0CLQYAAOADFQQAAEzYB4GAAACABWsQaDEAAAAfqCAAAGDCPggEBAAALHiKgYAAAIAFixRZgwAAAHygggAAgAlPMRAQAACwYJEiLQYAAOADFQQAAExoMRAQAACw4CkGWgwAAMAHKggAAJg0sEiRgAAAgBnxgBYDAADwgQoCAAAmPMVAQAAAwIKAQEAAAMCCnRRZgwAAAHygggAAgAktBgICAAAW7KRIiwEAAPhABQEAABMWKRIQAACwYA0CLQYAAOADFQQAAExoMRAQAACwoMVAiwEAAPhABQEAABP2QSAgAABg0cAaBAICAABmVBBYgwAAAHygggAAgAktBgICAAAWtBhoMQAAAB+oIAAAYEKLgYAAAIAFLQZaDAAAwAcqCAAAmNBiICAAAGBBi4EWAwAA8IEKAgAAJobREOgpBBwBAQAAkwZaDAQEAADMDBYpsgYBAIDWIj8/X1deeaUiIyPVsWNHjRo1Srt27fIaYxiG5s6dq4SEBIWFhWnQoEHauXOn1xiXy6Vp06apffv2ioiI0MiRI1VWVtakuRAQAAAwaZDht6MpSkpKNHXqVG3evFnFxcU6deqUMjMzdfz4cc+YgoICLVy4UIsXL9aWLVvkcDg0dOhQHT161DMmOztba9as0erVq7Vx40YdO3ZMI0aMUH19faPnYjNaSR1lTJdRgZ4C0Oo8N7d7oKcAtEphtxU06/1/Ft3Lb/c6ULXzpwedwaFDh9SxY0eVlJRowIABMgxDCQkJys7O1uzZsyWdrhbExcXpscce06RJk1RdXa0OHTpoxYoVGj16tCTp4MGD6ty5s9555x0NGzasUe9NBQEAgGbkcrlUU1Pjdbhcrka9trq6WpIUExMjSdq7d6+cTqcyMzM9Y+x2uwYOHKhNmzZJkkpLS1VXV+c1JiEhQcnJyZ4xjUFAAADApMEw/Hbk5+crKirK68jPz//JORiGoZycHP3iF79QcnKyJMnpdEqS4uLivMbGxcV5rjmdToWGhio6OvqMYxqDpxgAADDx506Kubm5ysnJ8Tpnt9t/8nV33XWXPvvsM23cuNFyzWazef1sGIblnFljxvwQFQQAAJqR3W5Xu3btvI6fCgjTpk3TW2+9pffff1+dOnXynHc4HJJkqQRUVFR4qgoOh0Nut1tVVVVnHNMYBAQAAEwMw/Db0dT3veuuu/T666/rvffeU2Jiotf1xMREORwOFRcXe8653W6VlJQoPT1dkpSamqqQkBCvMeXl5dqxY4dnTGPQYgAAwCRQOylOnTpVq1at0ptvvqnIyEhPpSAqKkphYWGy2WzKzs5WXl6ekpKSlJSUpLy8PIWHh2vs2LGesRMnTtSMGTMUGxurmJgYzZw5UykpKcrIyGj0XAgIAAC0EkuXLpUkDRo0yOv8888/r1tvvVWSNGvWLNXW1mrKlCmqqqpSWlqa1q9fr8jISM/4wsJCBQcHKysrS7W1tRoyZIiKiooUFBTU6LmwDwLQirEPAuBbc++D0L6d/373Kmu+8tu9WhIVBAAATBpax9/OAUVAAADApJUU1wOKpxgAAIAFFQQAAEwC9RRDa0JAAADAhBYDLQYAAOADFQQAAEx4ioGAAACAhT+/rOlsRYsBAABYUEEAAMCEFgMBAQAAC55ioMUAAAB8oIIAAIAJixQJCAAAWNBiICAAAGBBQGANAgAA8IEKAgAAJtQPJJtBHQU/4HK5lJ+fr9zcXNnt9kBPB2gV+L3AuYiAAC81NTWKiopSdXW12rVrF+jpAK0Cvxc4F7EGAQAAWBAQAACABQEBAABYEBDgxW6368EHH2QhFvAD/F7gXMQiRQAAYEEFAQAAWBAQAACABQEBAABYEBAAAIAFAQEeS5YsUWJios477zylpqbqo48+CvSUgID68MMPdd111ykhIUE2m01vvPFGoKcEtBgCAiRJL7/8srKzszVnzhxt3bpV/fv31/Dhw7Vv375ATw0ImOPHj+uyyy7T4sWLAz0VoMXxmCMkSWlpabriiiu0dOlSz7kePXpo1KhRys/PD+DMgNbBZrNpzZo1GjVqVKCnArQIKgiQ2+1WaWmpMjMzvc5nZmZq06ZNAZoVACCQCAhQZWWl6uvrFRcX53U+Li5OTqczQLMCAAQSAQEeNpvN62fDMCznAADnBgIC1L59ewUFBVmqBRUVFZaqAgDg3EBAgEJDQ5Wamqri4mKv88XFxUpPTw/QrAAAgRQc6AmgdcjJydH48ePVp08f9e3bV8uWLdO+ffs0efLkQE8NCJhjx47p66+/9vy8d+9ebdu2TTExMbrgggsCODOg+fGYIzyWLFmigoIClZeXKzk5WYWFhRowYECgpwUEzAcffKDBgwdbzk+YMEFFRUUtPyGgBREQAACABWsQAACABQEBAABYEBAAAIAFAQEAAFgQEAAAgAUBAQAAWBAQAACABQEBAABYEBAAAIAFAQEAAFgQEAAAgAUBAQAAWPx/j84cYZspG2MAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm=confusion_matrix(y_val, btahmin).astype(int)\n",
+    "\n",
+    "sns.heatmap(cm, annot=True, fmt='d')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "e3d38ce4-d45f-47fb-9748-bb3c475762cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L=LogisticRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "edf1407a-e073-48ff-9f42-7802e110fdd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\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-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\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",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">penalty&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;l2&#x27;</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\">dual&nbsp;</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\">tol&nbsp;</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('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</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('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</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\">intercept_scaling&nbsp;</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\">class_weight&nbsp;</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('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</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\">solver&nbsp;</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('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('multi_class',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">multi_class&nbsp;</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('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</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\">warm_start&nbsp;</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_jobs&nbsp;</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('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "d7f2f6a4-ae57-4da8-9339-f1fdc4ca98c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ltahmin=L.predict(x_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "08e2fbe7-ed84-4d82-bc1d-c9ec42ea0d35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7809085681426107"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_val,Ltahmin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "9e8d2b57-31d1-449c-a301-68f3d92741d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm=confusion_matrix(y_val, Ltahmin).astype(int)\n",
+    "\n",
+    "sns.heatmap(cm, annot=True, fmt='d')\n",
+    "plt.show();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "87b9e899-c782-496f-895a-a4b472f7e7ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.naive_bayes import BernoulliNB\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
+    "from sklearn.metrics import f1_score, confusion_matrix, classification_report\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "b = BernoulliNB()\n",
+    "l = LogisticRegression()\n",
+    "d = DecisionTreeClassifier()\n",
+    "r = RandomForestClassifier()\n",
+    "gb= GradientBoostingClassifier()\n",
+    "kn= KNeighborsClassifier()\n",
+    "ab= AdaBoostClassifier()\n",
+    "\n",
+    "def algo_test(x, y):\n",
+    "    modeller=[ b, l, d, r, gb, kn, ab]\n",
+    "    isimler=[\"BernoulliNB\", \"LogisticRegression\", \"DecisionTreeClassifier\", \n",
+    "             \"RandomForestClassifier\", \"GradientBoostingClassifier\", \"KNeighborsClassifier\",\n",
+    "             \"AdaBoostClassifier\"]\n",
+    "\n",
+    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.3, random_state = 42)\n",
+    "    \n",
+    "    accuracy = []\n",
+    "    precision = []\n",
+    "    recall = []\n",
+    "    f1 = []\n",
+    "    mdl=[]\n",
+    "\n",
+    "    print(\"Veriler hazır modeller deneniyor\")\n",
+    "    for model in modeller:\n",
+    "        print(model, \" modeli eğitiliyor!..\")\n",
+    "        model=model.fit(x_train,y_train)\n",
+    "        tahmin=model.predict(x_test)\n",
+    "        mdl.append(model)\n",
+    "        accuracy.append(accuracy_score(y_test, tahmin))\n",
+    "        precision.append(precision_score(y_test, tahmin, average=\"micro\"))\n",
+    "        recall.append(recall_score(y_test, tahmin, average=\"micro\"))\n",
+    "        f1.append(f1_score(y_test, tahmin, average=\"micro\"))\n",
+    "        print(confusion_matrix(y_test, tahmin))\n",
+    "\n",
+    "    print(\"Eğitim tamamlandı.\")\n",
+    "    \n",
+    "    metrics=pd.DataFrame(columns=[\"Accuracy\", \"Precision\", \"Recall\", \"F1\", \"Model\"], index=isimler)\n",
+    "    metrics[\"Accuracy\"] = accuracy\n",
+    "    metrics[\"Precision\"] = precision  \n",
+    "    metrics[\"Recall\"] = recall\n",
+    "    metrics[\"F1\"] = f1\n",
+    "    metrics[\"Model\"]=mdl\n",
+    "\n",
+    "    metrics.sort_values(\"F1\", ascending=False, inplace=True)\n",
+    "\n",
+    "    print(\"En başarılı model: \", metrics.iloc[0].name)\n",
+    "    model=metrics.iloc[0,-1]\n",
+    "    tahmin=model.predict(np.array(x_test) if model==kn else x_test)\n",
+    "    print(\"Confusion Matrix:\")\n",
+    "    print(confusion_matrix(y_test, tahmin))\n",
+    "    print(\"classification Report:\")\n",
+    "    print(classification_report(y_test, tahmin))\n",
+    "    print(\"Diğer Modeller:\")\n",
+    "    \n",
+    "    return metrics.drop(\"Model\", axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "8c1359eb-89cc-4232-b054-46594e9f5386",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Veriler hazır modeller deneniyor\n",
+      "BernoulliNB()  modeli eğitiliyor!..\n",
+      "[[1042  247]\n",
+      " [ 431  888]]\n",
+      "LogisticRegression()  modeli eğitiliyor!..\n",
+      "[[ 928  361]\n",
+      " [ 201 1118]]\n",
+      "DecisionTreeClassifier()  modeli eğitiliyor!..\n",
+      "[[930 359]\n",
+      " [337 982]]\n",
+      "RandomForestClassifier()  modeli eğitiliyor!..\n",
+      "[[1025  264]\n",
+      " [ 317 1002]]\n",
+      "GradientBoostingClassifier()  modeli eğitiliyor!..\n",
+      "[[ 957  332]\n",
+      " [ 217 1102]]\n",
+      "KNeighborsClassifier()  modeli eğitiliyor!..\n",
+      "[[ 950  339]\n",
+      " [ 276 1043]]\n",
+      "AdaBoostClassifier()  modeli eğitiliyor!..\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  File \"C:\\Users\\enesy\\anaconda3\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 257, in _count_physical_cores\n",
+      "    cpu_info = subprocess.run(\n",
+      "        \"wmic CPU Get NumberOfCores /Format:csv\".split(),\n",
+      "        capture_output=True,\n",
+      "        text=True,\n",
+      "    )\n",
+      "  File \"C:\\Users\\enesy\\anaconda3\\Lib\\subprocess.py\", line 554, in run\n",
+      "    with Popen(*popenargs, **kwargs) as process:\n",
+      "         ~~~~~^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\enesy\\anaconda3\\Lib\\subprocess.py\", line 1039, in __init__\n",
+      "    self._execute_child(args, executable, preexec_fn, close_fds,\n",
+      "    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "                        pass_fds, cwd, env,\n",
+      "                        ^^^^^^^^^^^^^^^^^^^\n",
+      "    ...<5 lines>...\n",
+      "                        gid, gids, uid, umask,\n",
+      "                        ^^^^^^^^^^^^^^^^^^^^^^\n",
+      "                        start_new_session, process_group)\n",
+      "                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\enesy\\anaconda3\\Lib\\subprocess.py\", line 1554, in _execute_child\n",
+      "    hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n",
+      "                       ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^\n",
+      "                             # no special security\n",
+      "                             ^^^^^^^^^^^^^^^^^^^^^\n",
+      "    ...<4 lines>...\n",
+      "                             cwd,\n",
+      "                             ^^^^\n",
+      "                             startupinfo)\n",
+      "                             ^^^^^^^^^^^^\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 976  313]\n",
+      " [ 274 1045]]\n",
+      "Eğitim tamamlandı.\n",
+      "En başarılı model:  GradientBoostingClassifier\n",
+      "Confusion Matrix:\n",
+      "[[ 957  332]\n",
+      " [ 217 1102]]\n",
+      "classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.82      0.74      0.78      1289\n",
+      "           1       0.77      0.84      0.80      1319\n",
+      "\n",
+      "    accuracy                           0.79      2608\n",
+      "   macro avg       0.79      0.79      0.79      2608\n",
+      "weighted avg       0.79      0.79      0.79      2608\n",
+      "\n",
+      "Diğer Modeller:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GradientBoostingClassifier</th>\n",
+       "      <td>0.789494</td>\n",
+       "      <td>0.789494</td>\n",
+       "      <td>0.789494</td>\n",
+       "      <td>0.789494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogisticRegression</th>\n",
+       "      <td>0.784509</td>\n",
+       "      <td>0.784509</td>\n",
+       "      <td>0.784509</td>\n",
+       "      <td>0.784509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForestClassifier</th>\n",
+       "      <td>0.777224</td>\n",
+       "      <td>0.777224</td>\n",
+       "      <td>0.777224</td>\n",
+       "      <td>0.777224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdaBoostClassifier</th>\n",
+       "      <td>0.774923</td>\n",
+       "      <td>0.774923</td>\n",
+       "      <td>0.774923</td>\n",
+       "      <td>0.774923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNeighborsClassifier</th>\n",
+       "      <td>0.764187</td>\n",
+       "      <td>0.764187</td>\n",
+       "      <td>0.764187</td>\n",
+       "      <td>0.764187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BernoulliNB</th>\n",
+       "      <td>0.740031</td>\n",
+       "      <td>0.740031</td>\n",
+       "      <td>0.740031</td>\n",
+       "      <td>0.740031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DecisionTreeClassifier</th>\n",
+       "      <td>0.733129</td>\n",
+       "      <td>0.733129</td>\n",
+       "      <td>0.733129</td>\n",
+       "      <td>0.733129</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            Accuracy  Precision    Recall        F1\n",
+       "GradientBoostingClassifier  0.789494   0.789494  0.789494  0.789494\n",
+       "LogisticRegression          0.784509   0.784509  0.784509  0.784509\n",
+       "RandomForestClassifier      0.777224   0.777224  0.777224  0.777224\n",
+       "AdaBoostClassifier          0.774923   0.774923  0.774923  0.774923\n",
+       "KNeighborsClassifier        0.764187   0.764187  0.764187  0.764187\n",
+       "BernoulliNB                 0.740031   0.740031  0.740031  0.740031\n",
+       "DecisionTreeClassifier      0.733129   0.733129  0.733129  0.733129"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo_test(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "ccfba44b-fc4f-4913-b667-4e3f3a04a687",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gb=GradientBoostingClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "cf1695b8-3016-4bee-ac7a-4a32b750ca07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\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-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\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",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">loss&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">subsample&nbsp;</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('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">criterion&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_leaf&nbsp;</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('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</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('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_depth&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_impurity_decrease&nbsp;</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('init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">init&nbsp;</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('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</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('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_features&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</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('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_leaf_nodes&nbsp;</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('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</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('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_iter_no_change&nbsp;</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('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</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('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "GradientBoostingClassifier()"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gb.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "13a1f4f2-5535-45b7-b94a-cc776934aa29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gbtahmin=gb.predict(x_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "63139e88-bd95-4794-ba00-86c86cde5281",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.78953421506613"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_val,gbtahmin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "5e03484e-ba0d-49cb-bfce-08ef021c2a38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "Not Transported       0.82      0.74      0.78       861\n",
+      "    Transported       0.77      0.84      0.80       878\n",
+      "\n",
+      "       accuracy                           0.79      1739\n",
+      "      macro avg       0.79      0.79      0.79      1739\n",
+      "   weighted avg       0.79      0.79      0.79      1739\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_val,gbtahmin,target_names=['Not Transported', 'Transported']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "3788f290-7511-49fe-b2ef-cee7bd7e1c58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['spaceship_titanic_gb.pkl']"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joblib.dump({'model': gb,'feature_columns': x.columns.tolist()},'spaceship_titanic_gb.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "c89d3a2b-7bad-495e-ab7a-aff2c92e174b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\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",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 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: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\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",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
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+       "\n",
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+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
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+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">loss&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;log_loss&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('learning_rate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('subsample',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">subsample&nbsp;</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('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">criterion&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;friedman_mse&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_samples_leaf&nbsp;</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('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</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('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_depth&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">min_impurity_decrease&nbsp;</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('init',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">init&nbsp;</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('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</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('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_features&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</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('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_leaf_nodes&nbsp;</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('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</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('validation_fraction',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
+       "            <td class=\"value\">0.1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_iter_no_change&nbsp;</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('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</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('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "</script></body>"
+      ],
+      "text/plain": [
+       "GradientBoostingClassifier()"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gb.fit(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "3bc397ef-074f-4473-9cec-c6ec03330e96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred=gb.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "c3f41f4c-8d91-4df5-a17f-a39ca66010d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission=pd.DataFrame({'PassengerId': passengerid,'Transported': pred.astype(bool)})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "f7256bce-d27d-4505-8edc-a469f8b36279",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission.to_csv('submission.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59947622-c377-48e2-a4c2-7b5c5a676dee",
+   "metadata": {},
+   "source": [
+    "### Conclusion\n",
+    "The Gradient Boosting Classifier achieved an accuracy of about 79% on the validation data.\n",
+    "The model showed balanced performance, with good precision and recall for both transported and not transported passengers.\n",
+    "These results indicate that the engineered features and the chosen model are effective for this classification problem."
+   ]
+  }
+ ],
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