diff --git a/Final_Project/201033_Swastika_FinalProject_2.ipynb b/Final_Project/201033_Swastika_FinalProject_2.ipynb
new file mode 100644
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+++ b/Final_Project/201033_Swastika_FinalProject_2.ipynb
@@ -0,0 +1,1426 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a6fccfcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f6c6964e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = pd.read_csv('train.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2cac26a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "</style>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
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+       "    </tr>\n",
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+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dd2231b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data = pd.read_csv('test.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3f6cd796",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kelly, Mr. James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>34.5</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+       "      <td>female</td>\n",
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+       "    <tr>\n",
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+       "      <td>2</td>\n",
+       "      <td>Myles, Mr. Thomas Francis</td>\n",
+       "      <td>male</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>9.6875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wirz, Mr. Albert</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>NaN</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101298</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass                                          Name     Sex  \\\n",
+       "0          892       3                              Kelly, Mr. James    male   \n",
+       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
+       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
+       "3          895       3                              Wirz, Mr. Albert    male   \n",
+       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
+       "\n",
+       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
+       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
+       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
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+       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
+       "4  22.0      1      1  3101298  12.2875   NaN        S  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3712ca63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(data=train_data, y='Age', x='Fare', kind='scatter')\n",
+    "\n",
+    "plt.ylabel('Age')\n",
+    "plt.xlabel('Fare')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8a9b010a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(data=train_data, x='Survived')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a21f0009",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Survived         0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age            177\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          687\n",
+       "Embarked         2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "83a3f9cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age             86\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             1\n",
+       "Cabin          327\n",
+       "Embarked         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c16cb400",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0, 0.5, '')]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, sharey=True)\n",
+    "fig.set_size_inches([9,3])\n",
+    "sns.countplot(x=\"Pclass\", hue='Survived',data=train_data, ax=ax0).legend([\"Dead\",\"Survived\"])\n",
+    "sns.countplot(x=\"Sex\", hue='Survived',data=train_data, ax=ax1).legend([\"Dead\",\"Survived\"]); ax1.set(ylabel='')\n",
+    "sns.countplot(x=\"Embarked\", hue='Survived',data=train_data, ax=ax2).legend([\"Dead\",\"Survived\"]); ax2.set(ylabel='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bf2411a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1be91c57280>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(x=\"Age\", hue='Survived', hue_order = [1,0],data=train_data).legend([\"Dead\",\"Survived\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "998898bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1be91cd9460>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.FacetGrid(train_data, col=\"Sex\", hue=\"Survived\", height=4)\n",
+    "\n",
+    "combined_age = train_data['Age'].dropna()\n",
+    "_, bin_edges = np.histogram(combined_age, bins=10)\n",
+    "\n",
+    "g.map(plt.hist, \"Age\", bins=bin_edges, alpha=0.6, edgecolor=\"black\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f10625e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1be91f7d1f0>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.FacetGrid(train_data, col=\"Pclass\", hue=\"Survived\", height=4)\n",
+    "\n",
+    "combined_age = train_data['Age'].dropna()\n",
+    "_, bin_edges = np.histogram(combined_age, bins=10)\n",
+    "\n",
+    "g.map(plt.hist, \"Age\", bins=bin_edges, alpha=0.6, edgecolor=\"black\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e34fcafd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1be923e19a0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.FacetGrid(train_data, col=\"Embarked\", hue=\"Survived\", height=4)\n",
+    "\n",
+    "combined_age = train_data['Age'].dropna()\n",
+    "_, bin_edges = np.histogram(combined_age, bins=10)\n",
+    "\n",
+    "g.map(plt.hist, \"Age\", bins=bin_edges, alpha=0.6, edgecolor=\"black\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c3e17b55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1be924292b0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(x=\"Fare\", hue='Survived', hue_order = [1,0],data=train_data).legend([\"Dead\",\"Survived\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "880e4782",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Swastika\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6607: RuntimeWarning: All-NaN slice encountered\n",
+      "  xmin = min(xmin, np.nanmin(xi))\n",
+      "C:\\Users\\Swastika\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6608: RuntimeWarning: All-NaN slice encountered\n",
+      "  xmax = max(xmax, np.nanmax(xi))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1be9184ea00>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2800x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.FacetGrid(train_data, col=\"SibSp\", hue=\"Survived\", height=4)\n",
+    "\n",
+    "combined_age = train_data['Age'].dropna()\n",
+    "_, bin_edges = np.histogram(combined_age, bins=10)\n",
+    "\n",
+    "g.map(plt.hist, \"Age\", bins=bin_edges, alpha=0.6, edgecolor=\"black\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0c604f49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1be9407f070>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2800x400 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = sns.FacetGrid(train_data, col=\"Parch\", hue=\"Survived\", height=4)\n",
+    "\n",
+    "combined_age = train_data['Age'].dropna()\n",
+    "_, bin_edges = np.histogram(combined_age, bins=10)\n",
+    "\n",
+    "g.map(plt.hist, \"Age\", bins=bin_edges, alpha=0.6, edgecolor=\"black\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "246ec7e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data['Age'] = train_data['Age'].fillna(value = round(train_data.Age.mean()))\n",
+    "train_data['Fare'] = train_data['Fare'].fillna(value = 0)\n",
+    "train_data['Cabin'] = train_data['Cabin'].fillna(value = \"NA\")\n",
+    "train_data['Embarked'] = train_data['Embarked'].fillna(value = \"NA\")\n",
+    "\n",
+    "test_data['Age'] = test_data['Age'].fillna(value = round(train_data.Age.mean()))\n",
+    "test_data['Fare'] = test_data['Fare'].fillna(value = 0)\n",
+    "test_data['Cabin'] = test_data['Cabin'].fillna(value = \"NA\")\n",
+    "test_data['Embarked'] = test_data['Embarked'].fillna(value = \"NA\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "edffc455",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data_new = pd.get_dummies(train_data, columns=['Sex', 'Embarked'], drop_first=True)\n",
+    "train_data_new['TicketPrefix'] = train_data_new['Ticket'].str.extract('([A-Za-z]+)[^\\w]', expand=False)\n",
+    "train_data_new['TicketLength'] = train_data_new['Ticket'].str.len()\n",
+    "train_data_new['IsNumericTicket'] = train_data_new['Ticket'].str.isnumeric().astype(int)\n",
+    "train_data_new.drop('Ticket', axis=1, inplace=True)\n",
+    "train_data_new['Salutation'] = train_data_new['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
+    "train_data_new = pd.get_dummies(train_data_new, columns=['Salutation'], prefix='Salutation', drop_first=True)\n",
+    "train_data_new.drop('Name', axis=1, inplace=True)\n",
+    "train_data_new['Deck'] = train_data_new['Cabin'].str.slice(0, 1)\n",
+    "train_data_new = pd.get_dummies(train_data_new, columns=['Deck'], prefix='Deck', drop_first=True)\n",
+    "train_data_new['HasCabin'] = train_data_new['Cabin'].notnull().astype(int)\n",
+    "train_data_new.drop('Cabin', axis=1, inplace=True)\n",
+    "encoded_columns = train_data_new.drop('Survived', axis=1).columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ba5f0e0a",
+   "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>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Sex_male</th>\n",
+       "      <th>Embarked_NA</th>\n",
+       "      <th>Embarked_Q</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Salutation_Sir</th>\n",
+       "      <th>Deck_B</th>\n",
+       "      <th>Deck_C</th>\n",
+       "      <th>Deck_D</th>\n",
+       "      <th>Deck_E</th>\n",
+       "      <th>Deck_F</th>\n",
+       "      <th>Deck_G</th>\n",
+       "      <th>Deck_N</th>\n",
+       "      <th>Deck_T</th>\n",
+       "      <th>HasCabin</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  Sex_male  \\\n",
+       "0            1         0       3  22.0      1      0   7.2500         1   \n",
+       "1            2         1       1  38.0      1      0  71.2833         0   \n",
+       "2            3         1       3  26.0      0      0   7.9250         0   \n",
+       "3            4         1       1  35.0      1      0  53.1000         0   \n",
+       "4            5         0       3  35.0      0      0   8.0500         1   \n",
+       "\n",
+       "   Embarked_NA  Embarked_Q  ...  Salutation_Sir Deck_B  Deck_C  Deck_D  \\\n",
+       "0            0           0  ...               0      0       0       0   \n",
+       "1            0           0  ...               0      0       1       0   \n",
+       "2            0           0  ...               0      0       0       0   \n",
+       "3            0           0  ...               0      0       1       0   \n",
+       "4            0           0  ...               0      0       0       0   \n",
+       "\n",
+       "   Deck_E  Deck_F  Deck_G  Deck_N  Deck_T  HasCabin  \n",
+       "0       0       0       0       1       0         1  \n",
+       "1       0       0       0       0       0         1  \n",
+       "2       0       0       0       1       0         1  \n",
+       "3       0       0       0       0       0         1  \n",
+       "4       0       0       0       1       0         1  \n",
+       "\n",
+       "[5 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "9c26e3d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_male',\n",
+       "       'Embarked_NA', 'Embarked_Q', 'Embarked_S', 'TicketPrefix',\n",
+       "       'TicketLength', 'IsNumericTicket', 'Salutation_Col',\n",
+       "       'Salutation_Countess', 'Salutation_Don', 'Salutation_Dr',\n",
+       "       'Salutation_Jonkheer', 'Salutation_Lady', 'Salutation_Major',\n",
+       "       'Salutation_Master', 'Salutation_Miss', 'Salutation_Mlle',\n",
+       "       'Salutation_Mme', 'Salutation_Mr', 'Salutation_Mrs', 'Salutation_Ms',\n",
+       "       'Salutation_Rev', 'Salutation_Sir', 'Deck_B', 'Deck_C', 'Deck_D',\n",
+       "       'Deck_E', 'Deck_F', 'Deck_G', 'Deck_N', 'Deck_T', 'HasCabin'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoded_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b2605ade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f8afa6dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8212290502793296\n",
+      "Precision: 0.8088235294117647\n",
+      "Recall: 0.7432432432432432\n",
+      "Confusion Matrix:\n",
+      "[[92 13]\n",
+      " [19 55]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = train_data_new.drop('Survived', axis=1)\n",
+    "y = train_data_new['Survived']\n",
+    "\n",
+    "categorical_columns = X.select_dtypes(include=['object']).columns\n",
+    "\n",
+    "imputer = SimpleImputer(strategy='most_frequent')\n",
+    "X_imputed = imputer.fit_transform(X)\n",
+    "X_imputed = pd.DataFrame(X_imputed, columns=X.columns)\n",
+    "X_encoded = pd.get_dummies(X_imputed, columns=categorical_columns)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "model = RandomForestClassifier()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "\n",
+    "precision = metrics.precision_score(y_test, y_pred)\n",
+    "print(\"Precision:\", precision)\n",
+    "\n",
+    "recall = metrics.recall_score(y_test, y_pred)\n",
+    "print(\"Recall:\", recall)\n",
+    "\n",
+    "confusion = metrics.confusion_matrix(y_test, y_pred)\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e3872783",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_new = pd.get_dummies(test_data, columns=['Sex', 'Embarked'], drop_first=True)\n",
+    "test_new['TicketPrefix'] = test_new['Ticket'].str.extract('([A-Za-z]+)[^\\w]', expand=False)\n",
+    "test_new = pd.get_dummies(test_new, columns=['TicketPrefix'], prefix='TicketPrefix', drop_first=True)\n",
+    "test_new['TicketLength'] = test_new['Ticket'].str.len()\n",
+    "test_new['IsNumericTicket'] = test_new['Ticket'].str.isnumeric().astype(int)\n",
+    "test_new.drop('Ticket', axis=1, inplace=True)\n",
+    "test_new['Salutation'] = test_new['Name'].str.extract(' ([A-Za-z]+).', expand=False)\n",
+    "test_new = pd.get_dummies(test_new, columns=['Salutation'], prefix='Salutation', drop_first=True)\n",
+    "test_new.drop('Name', axis=1, inplace=True)\n",
+    "test_new['Deck'] = test_new['Cabin'].str.slice(0, 1)\n",
+    "test_new = pd.get_dummies(test_new, columns=['Deck'], prefix='Deck', drop_first=True)\n",
+    "test_new['HasCabin'] = test_new['Cabin'].notnull().astype(int)\n",
+    "test_new.drop('Cabin', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f078c516",
+   "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>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Sex_male</th>\n",
+       "      <th>Embarked_Q</th>\n",
+       "      <th>Embarked_S</th>\n",
+       "      <th>TicketPrefix_AQ</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Salutation_Rev</th>\n",
+       "      <th>Salutation_y</th>\n",
+       "      <th>Deck_B</th>\n",
+       "      <th>Deck_C</th>\n",
+       "      <th>Deck_D</th>\n",
+       "      <th>Deck_E</th>\n",
+       "      <th>Deck_F</th>\n",
+       "      <th>Deck_G</th>\n",
+       "      <th>Deck_N</th>\n",
+       "      <th>HasCabin</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>34.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.8292</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>2</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.6875</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.6625</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass   Age  SibSp  Parch     Fare  Sex_male  Embarked_Q  \\\n",
+       "0          892       3  34.5      0      0   7.8292         1           1   \n",
+       "1          893       3  47.0      1      0   7.0000         0           0   \n",
+       "2          894       2  62.0      0      0   9.6875         1           1   \n",
+       "3          895       3  27.0      0      0   8.6625         1           0   \n",
+       "4          896       3  22.0      1      1  12.2875         0           0   \n",
+       "\n",
+       "   Embarked_S  TicketPrefix_AQ  ...  Salutation_Rev  Salutation_y  Deck_B  \\\n",
+       "0           0                0  ...               0             0       0   \n",
+       "1           1                0  ...               0             0       0   \n",
+       "2           0                0  ...               0             0       0   \n",
+       "3           1                0  ...               0             0       0   \n",
+       "4           1                0  ...               0             0       0   \n",
+       "\n",
+       "   Deck_C  Deck_D  Deck_E  Deck_F  Deck_G  Deck_N  HasCabin  \n",
+       "0       0       0       0       0       0       1         1  \n",
+       "1       0       0       0       0       0       1         1  \n",
+       "2       0       0       0       0       0       1         1  \n",
+       "3       0       0       0       0       0       1         1  \n",
+       "4       0       0       0       0       0       1         1  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "2a310df5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "realigned_test = test_new.reindex(columns=encoded_columns, fill_value=0)\n",
+    "imputed_test = pd.DataFrame(imputer.transform(realigned_test), columns=realigned_test.columns)\n",
+    "imputed_test = imputed_test.reindex(columns=X_train.columns, fill_value=0)\n",
+    "predicted_test = model.predict(imputed_test)\n",
+    "\n",
+    "# Creating a DataFrame with 'PassengerId' and 'Survived' columns\n",
+    "result_df = pd.DataFrame({\n",
+    "    'PassengerId': test_data['PassengerId'],\n",
+    "    'Survived': predicted_test\n",
+    "})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "22a07918",
+   "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>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived\n",
+       "0          892         0\n",
+       "1          893         1\n",
+       "2          894         0\n",
+       "3          895         0\n",
+       "4          896         1"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_df.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}