{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3f22ce5d-68a7-4b8d-9bd4-90d5fc48b579",
   "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": 40,
   "id": "854239b3-0aac-4d51-895e-6578269c2bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'Titanic-Dataset.csv'\n",
    "df = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c1b46367-854c-4e25-918d-efcbe7e0a3a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'Titanic-Dataset.csv'\n",
    "df = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "04294db9-09ed-4fb9-b83b-72e7ba7fd8fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 5 rows of the DataFrame:\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>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",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>STON/O2. 3101282</td>\n",
       "      <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",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\nFirst 5 rows of the DataFrame:\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "739759a5-f3d2-4ec2-bff2-c842c740338f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Handling Missing Values ---\n",
      "Missing values before handling:\n",
      "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\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n--- Handling Missing Values ---\")\n",
    "print(\"Missing values before handling:\")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1a1a873a-af22-4c36-9db0-be2eb9e32c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = df['Age'].fillna(df['Age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5f28a83f-85c9-49e7-ba95-6672fe9a758e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Embarked'] = df['Embarked'].fillna(df['Embarked'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2016949e-e58e-4da6-9ba4-c13b41561709",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "506c7133-01aa-4aec-ba06-b14712ecc72d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Missing values after handling:\n",
      "PassengerId    0\n",
      "Survived       0\n",
      "Pclass         0\n",
      "Name           0\n",
      "Sex            0\n",
      "Age            0\n",
      "SibSp          0\n",
      "Parch          0\n",
      "Ticket         0\n",
      "Fare           0\n",
      "Embarked       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nMissing values after handling:\")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "59e02563-a58f-4a30-a93e-20654acb7c7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Handling Outliers ---\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n--- Handling Outliers ---\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ed0c5682-39aa-4aab-b1b4-3e83ab0b4b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1_fare = df['Fare'].quantile(0.25)\n",
    "Q3_fare = df['Fare'].quantile(0.75)\n",
    "IQR_fare = Q3_fare - Q1_fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8eb8b7e5-3eb5-4023-965a-ebb5adbdf75f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bound_fare = Q1_fare - 1.5 * IQR_fare\n",
    "upper_bound_fare = Q3_fare + 1.5 * IQR_fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c78c5a0f-9d1d-4b51-9c88-c141c144130c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers in 'Fare' column before capping: 116\n"
     ]
    }
   ],
   "source": [
    "outliers_fare = df[(df['Fare'] < lower_bound_fare) | (df['Fare'] > upper_bound_fare)]\n",
    "print(f\"Number of outliers in 'Fare' column before capping: {len(outliers_fare)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "df8af458-ecd0-49e4-929b-f2f45d6fd731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers in 'Fare' column have been capped.\n"
     ]
    }
   ],
   "source": [
    "df['Fare'] = np.where(df['Fare'] < lower_bound_fare, lower_bound_fare, df['Fare'])\n",
    "df['Fare'] = np.where(df['Fare'] > upper_bound_fare, upper_bound_fare, df['Fare'])\n",
    "\n",
    "print(\"Outliers in 'Fare' column have been capped.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e13fdb6d-9833-4ffd-aa0f-c5fb0220dc8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Data Visualization - Part 1 \n"
     ]
    }
   ],
   "source": [
    "print(\"\\n--- Data Visualization - Part 1 \")\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "39631b76-b330-4048-b8d9-9cb302a79f9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 0 Axes>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0c0d6d33-20ec-439a-8aef-9c4b90d08986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1) # 1 row, 2 columns, first plot\n",
    "sns.histplot(df['Age'], kde=True, bins=30, color='skyblue')\n",
    "plt.title('Distribution of Age after Imputation', fontsize=16)\n",
    "plt.xlabel('Age', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "be0e6841-77c7-422c-a8b9-992cc19312e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x218e79d9f70>,\n",
       "  <matplotlib.axis.XTick at 0x218e5337560>],\n",
       " [Text(0, 0, 'Not Survived'), Text(1, 0, 'Survived')])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 2) \n",
    "sns.countplot(x='Survived', data=df, palette='viridis', hue='Survived', legend=False)\n",
    "plt.title('Count of Survivors (0: Not Survived, 1: Survived)', fontsize=16)\n",
    "plt.xlabel('Survived', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "# Set custom x-axis tick labels for clarity\n",
    "plt.xticks(ticks=[0, 1], labels=['Not Survived', 'Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "19eb216c-6717-42a0-88a6-373de3417e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Data Visualization - Part 2 ---\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 0 Axes>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(\"\\n--- Data Visualization - Part 2 ---\")\n",
    "plt.figure(figsize=(15, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "26fde865-18de-4847-a3b6-b83fe9d4e85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "473b2b48-168e-4159-907b-a55dd68933b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Data Visualization - Part 2 ---\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 0 Axes>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n--- Data Visualization - Part 2 ---\")\n",
    "plt.figure(figsize=(15, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "34595604-ce65-46e6-9f76-b489c9105ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Survival Rate')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "sns.barplot(x='Sex', y='Survived', data=df, palette='cividis', hue='Sex', legend=False)\n",
    "plt.title('Survival Rate by Sex', fontsize=16)\n",
    "plt.xlabel('Sex', fontsize=12)\n",
    "plt.ylabel('Survival Rate', fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b43db34b-5ec1-42dd-8538-76a8829f7655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 2) \n",
    "sns.barplot(x='Pclass', y='Survived', data=df, palette='magma', hue='Pclass', legend=False)\n",
    "plt.title('Survival Rate by Passenger Class', fontsize=16)\n",
    "plt.xlabel('Passenger Class', fontsize=12)\n",
    "plt.ylabel('Survival Rate', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e06de112-a397-4537-8885-c4150b3113a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Data Visualization - Part 3 ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n--- Data Visualization - Part 3 ---\")\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['Fare'], kde=True, bins=30, color='lightcoral')\n",
    "plt.title('Distribution of Fare after Outlier Capping', fontsize=16)\n",
    "plt.xlabel('Fare', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b2721fb6-dffd-44bb-a4dd-c09819afbfd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Final Output ---\n",
      "Processed dataset saved to: Titanic_Processed_Dataset.csv\n",
      "\n",
      "Final DataFrame Info after all operations:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 76.7+ KB\n",
      "\n",
      "First 5 rows of the processed DataFrame:\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>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>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",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>65.6344</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",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</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",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </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>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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  65.6344        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\n--- Final Output ---\")\n",
    "output_file_name = 'Titanic_Processed_Dataset.csv'\n",
    "df.to_csv(output_file_name, index=False)\n",
    "print(f\"Processed dataset saved to: {output_file_name}\")\n",
    "\n",
    "print(\"\\nFinal DataFrame Info after all operations:\")\n",
    "df.info()\n",
    "\n",
    "print(\"\\nFirst 5 rows of the processed DataFrame:\")\n",
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f4d906b-37b2-4ab8-88da-31e1c0d887fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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