{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8fe04b42-ec78-4787-a5a6-a0b99bddd9ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90cab4c4-13e3-4f14-bf8f-dcf79e69ae6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load your dataset into a DataFrame\n",
    "df = pd.read_csv('Titanic-Dataset.csv')\n",
    "# Preview the first 5 rows\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5a51921-097e-46c3-af60-92702a9ee0c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e304443d-2729-43f3-8a86-55e02c88e5b4",
   "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",
       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</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>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37407720-927e-4756-b744-8eea36ce253a",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f98491cf-bf13-45ad-b434-a0487c96b2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3dc84ffb-0063-4dc0-ab24-6fafd4c95696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with more than 30% missing values:\n",
      "Cabin    77.104377\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "missing_percentage = df.isnull().mean() * 100\n",
    "# Filter columns with more than 30% (or 50%) missing values\n",
    "columns_with_missing_30 = missing_percentage[missing_percentage > 50]\n",
    "# Display the columns with more than 30% missing values\n",
    "print(\"Columns with more than 30% missing values:\")\n",
    "print(columns_with_missing_30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5212fa72-b6f1-48ba-a3c9-5757c78fcba4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns dropped: ['Cabin']\n",
      "Remaining columns: Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "missing_percentage = df.isnull().mean() * 100\n",
    "threshold = 50\n",
    "# Filter columns with more than the threshold percentage of missing values\n",
    "columns_to_drop = missing_percentage[missing_percentage > threshold].index\n",
    "# Drop the columns\n",
    "df_cleaned = df.drop(columns=columns_to_drop)\n",
    "# Display the resulting dataframe after removing columns with too many missing valu\n",
    "print(f\"Columns dropped: {list(columns_to_drop)}\")\n",
    "print(f\"Remaining columns: {df_cleaned.columns}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21c71b7b-225a-4899-9127-594828ab8db5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A    B     C\n",
      "0  1.0  5.0   9.0\n",
      "1  2.0  6.0  10.0\n",
      "2  3.0  7.0  11.0\n",
      "3  4.0  8.0  10.5\n"
     ]
    }
   ],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "# Example dataset\n",
    "data = pd.DataFrame({\n",
    " 'A': [1, 2, None, 4],\n",
    " 'B': [5, None, 7, 8],\n",
    " 'C': [9, 10, 11, None]\n",
    "})\n",
    "# KNN imputation (use n_neighbors=2, you can adjust this)\n",
    "imputer = KNNImputer(n_neighbors=2)\n",
    "data_imputed_knn = pd.DataFrame(imputer.fit_transform(data), columns=data.columns)\n",
    "print(data_imputed_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8613cae9-47ee-4219-8ce9-d984310386a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "75daa5ca-04c8-4bdf-b297-a24a49e35f81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>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>108</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>669</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>472</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Name  Sex   Age  SibSp  Parch  Ticket  \\\n",
       "0            1         0       3   108    1  22.0      1      0     523   \n",
       "1            2         1       1   190    0  38.0      1      0     596   \n",
       "2            3         1       3   353    0  26.0      0      0     669   \n",
       "3            4         1       1   272    0  35.0      1      0      49   \n",
       "4            5         0       3    15    1  35.0      0      0     472   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0   7.2500    147         2  \n",
       "1  71.2833     81         0  \n",
       "2   7.9250    147         2  \n",
       "3  53.1000     55         2  \n",
       "4   8.0500    147         2  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    " df[column] = label_encoder.fit_transform(df[column])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a36b6bf3-5b18-4e42-87cd-04965218eb81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in columns with mode\n",
    "for column in df.select_dtypes(include=['float64']).columns:\n",
    " mean_value = df[column].mean() # Get the mode (most frequent value)\n",
    " df[column] = df[column].fillna(mean_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "23b7c39d-afb0-4362-83bf-d75d3c4995bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in categorical columns with mode\n",
    "for column in df.select_dtypes(include=['object']).columns:\n",
    " mode_value = df[column].mode()[0] # Get the mode (most frequent value)\n",
    " df[column] = df[column].fillna(mode_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "10e7095c-cee8-41d2-93be-f34f40acf720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5d57b78-b2a1-422a-a43b-fc6163179b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "        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",
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       "      <td>0</td>\n",
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       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.9250</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>53.1000</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>303</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>413</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>675</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
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       "      <td>3</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>466</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  Name  Sex        Age  SibSp  Parch  \\\n",
       "0              1         0       3   108    1  22.000000      1      0   \n",
       "1              2         1       1   190    0  38.000000      1      0   \n",
       "2              3         1       3   353    0  26.000000      0      0   \n",
       "3              4         1       1   272    0  35.000000      1      0   \n",
       "4              5         0       3    15    1  35.000000      0      0   \n",
       "..           ...       ...     ...   ...  ...        ...    ...    ...   \n",
       "886          887         0       2   548    1  27.000000      0      0   \n",
       "887          888         1       1   303    0  19.000000      0      0   \n",
       "888          889         0       3   413    0  29.699118      1      2   \n",
       "889          890         1       1    81    1  26.000000      0      0   \n",
       "890          891         0       3   220    1  32.000000      0      0   \n",
       "\n",
       "     Ticket     Fare  Cabin  Embarked  \n",
       "0       523   7.2500    147         2  \n",
       "1       596  71.2833     81         0  \n",
       "2       669   7.9250    147         2  \n",
       "3        49  53.1000     55         2  \n",
       "4       472   8.0500    147         2  \n",
       "..      ...      ...    ...       ...  \n",
       "886     101  13.0000    147         2  \n",
       "887      14  30.0000     30         2  \n",
       "888     675  23.4500    147         2  \n",
       "889       8  30.0000     60         0  \n",
       "890     466   7.7500    147         1  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78161add-95c4-4c2c-8fe4-b31c09e1cb1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_outliers_with_nan(column):\n",
    " Q1 = column.quantile(0.25)\n",
    " Q3 = column.quantile(0.75)\n",
    " IQR = Q3 - Q1\n",
    " lower_bound = Q1 - 1.5 * IQR\n",
    " upper_bound = Q3 + 1.5 * IQR\n",
    " # Replace outliers with NaN\n",
    " return column.where((column >= lower_bound) & (column <= upper_bound), np.nan)\n",
    "# Replace outliers with NaN for each feature\n",
    "for col in df.columns:\n",
    " df[col] = replace_outliers_with_nan(df[col])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a7a339a4-e770-4adf-b8a0-419173a15894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of null values in each column:\n",
      "A    1\n",
      "B    1\n",
      "C    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "null_counts = data.isnull().sum()\n",
    "print(\"Sum of null values in each column:\")\n",
    "print(null_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fc93d20d-d533-4cd8-b7c6-b0d7ae06111d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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",
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       "      <th>Fare</th>\n",
       "      <th>...</th>\n",
       "      <th>Pclass_Outlier</th>\n",
       "      <th>Name_Outlier</th>\n",
       "      <th>Sex_Outlier</th>\n",
       "      <th>Age_Outlier</th>\n",
       "      <th>SibSp_Outlier</th>\n",
       "      <th>Parch_Outlier</th>\n",
       "      <th>Ticket_Outlier</th>\n",
       "      <th>Fare_Outlier</th>\n",
       "      <th>Cabin_Outlier</th>\n",
       "      <th>Embarked_Outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
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       "      <td>837</td>\n",
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       "      <td>18.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>302</td>\n",
       "      <td>18.0000</td>\n",
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       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>190</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>620</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>440</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>61.1750</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>308</td>\n",
       "      <td>1</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>612</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>362</td>\n",
       "      <td>1</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>676</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>595</td>\n",
       "      <td>61.3792</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>539</td>\n",
       "      <td>1</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>518</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>441</td>\n",
       "      <td>23.2500</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>710</td>\n",
       "      <td>0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>517</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>441</td>\n",
       "      <td>23.2500</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>334</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>838</td>\n",
       "      <td>1</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>393</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>313</td>\n",
       "      <td>1</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>437</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>253</td>\n",
       "      <td>0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>676</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>438</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>688</td>\n",
       "      <td>0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>449</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>514</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>705</td>\n",
       "      <td>0</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>599</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>530</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>366</td>\n",
       "      <td>1</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>235</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>566</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>204</td>\n",
       "      <td>1</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>519</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>572</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>51.4792</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>601</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>391</td>\n",
       "      <td>0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>616</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>357</td>\n",
       "      <td>0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>619</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>656</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>661</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>666</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>361</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>743</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>711</td>\n",
       "      <td>0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>755</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>358</td>\n",
       "      <td>0</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>861</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>325</td>\n",
       "      <td>1</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>399</td>\n",
       "      <td>14.1083</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  Name  Sex        Age  SibSp  Parch  \\\n",
       "38            39         0       3   837    0  18.000000    2.0    0.0   \n",
       "48            49         0       3   726    1  29.699118    2.0    0.0   \n",
       "54            55         0       1   620    1        NaN    0.0    NaN   \n",
       "69            70         0       3   440    1  26.000000    2.0    0.0   \n",
       "92            93         0       1   150    1  46.000000    1.0    0.0   \n",
       "97            98         1       1   308    1  23.000000    0.0    NaN   \n",
       "104          105         0       3   312    1  37.000000    2.0    0.0   \n",
       "120          121         0       2   362    1  21.000000    2.0    0.0   \n",
       "147          148         0       3   254    0   9.000000    2.0    NaN   \n",
       "155          156         0       1   866    1  51.000000    0.0    NaN   \n",
       "183          184         1       2    76    1        NaN    2.0    NaN   \n",
       "245          246         0       1   539    1  44.000000    2.0    0.0   \n",
       "301          302         1       3   518    1  29.699118    2.0    0.0   \n",
       "311          312         1       1   710    0  18.000000    2.0    NaN   \n",
       "330          331         1       3   517    0  29.699118    2.0    0.0   \n",
       "333          334         0       3   838    1  16.000000    2.0    0.0   \n",
       "392          393         0       3   313    1  28.000000    2.0    0.0   \n",
       "436          437         0       3   253    0  21.000000    2.0    NaN   \n",
       "437          438         1       2   688    0  24.000000    2.0    NaN   \n",
       "448          449         1       3    55    0   5.000000    2.0    NaN   \n",
       "469          470         1       3    54    0        NaN    2.0    NaN   \n",
       "513          514         1       1   705    0  54.000000    1.0    0.0   \n",
       "529          530         0       2   366    1  23.000000    2.0    NaN   \n",
       "565          566         0       3   204    1  24.000000    2.0    0.0   \n",
       "571          572         1       1    36    0  53.000000    2.0    0.0   \n",
       "600          601         1       2   391    0  24.000000    2.0    NaN   \n",
       "615          616         1       2   357    0  24.000000    1.0    NaN   \n",
       "618          619         1       2    77    0   4.000000    2.0    NaN   \n",
       "644          645         1       3    53    0        NaN    2.0    NaN   \n",
       "655          656         0       2   360    1  24.000000    2.0    0.0   \n",
       "660          661         1       1   264    1  50.000000    2.0    0.0   \n",
       "665          666         0       2   361    1  32.000000    2.0    0.0   \n",
       "742          743         1       1   711    0  21.000000    2.0    NaN   \n",
       "754          755         1       2   358    0  48.000000    1.0    NaN   \n",
       "860          861         0       3   325    1  41.000000    2.0    0.0   \n",
       "\n",
       "     Ticket     Fare  ...  Pclass_Outlier  Name_Outlier  Sex_Outlier  \\\n",
       "38      302  18.0000  ...           False         False        False   \n",
       "48      190  21.6792  ...           False         False        False   \n",
       "54       29  61.9792  ...           False         False        False   \n",
       "69      270   8.6625  ...           False         False        False   \n",
       "92      678  61.1750  ...           False         False        False   \n",
       "97      612  63.3583  ...           False         False        False   \n",
       "104     245   7.9250  ...           False         False        False   \n",
       "120     621      NaN  ...           False         False        False   \n",
       "147     676  34.3750  ...           False         False        False   \n",
       "155     595  61.3792  ...           False         False        False   \n",
       "183     114  39.0000  ...           False         False        False   \n",
       "245      92      NaN  ...           False         False        False   \n",
       "301     441  23.2500  ...           False         False        False   \n",
       "311     602      NaN  ...           False         False        False   \n",
       "330     441  23.2500  ...           False         False        False   \n",
       "333     302  18.0000  ...           False         False        False   \n",
       "392     246   7.9250  ...           False         False        False   \n",
       "436     676  34.3750  ...           False         False        False   \n",
       "437     237  18.7500  ...           False         False        False   \n",
       "448     194  19.2583  ...           False         False        False   \n",
       "469     194  19.2583  ...           False         False        False   \n",
       "513     599  59.4000  ...           False         False        False   \n",
       "529     235  11.5000  ...           False         False        False   \n",
       "565     519  24.1500  ...           False         False        False   \n",
       "571      60  51.4792  ...           False         False        False   \n",
       "600     142  27.0000  ...           False         False        False   \n",
       "615     106  65.0000  ...           False         False        False   \n",
       "618     114  39.0000  ...           False         False        False   \n",
       "644     194  19.2583  ...           False         False        False   \n",
       "655     621      NaN  ...           False         False        False   \n",
       "660     605      NaN  ...           False         False        False   \n",
       "665     621      NaN  ...           False         False        False   \n",
       "742     602      NaN  ...           False         False        False   \n",
       "754     106  65.0000  ...           False         False        False   \n",
       "860     399  14.1083  ...           False         False        False   \n",
       "\n",
       "     Age_Outlier  SibSp_Outlier  Parch_Outlier  Ticket_Outlier  Fare_Outlier  \\\n",
       "38         False           True          False           False         False   \n",
       "48         False           True          False           False         False   \n",
       "54         False          False          False           False          True   \n",
       "69         False           True          False           False         False   \n",
       "92         False          False          False           False          True   \n",
       "97         False          False          False           False          True   \n",
       "104        False           True          False           False         False   \n",
       "120        False           True          False           False         False   \n",
       "147        False           True          False           False         False   \n",
       "155        False          False          False           False          True   \n",
       "183        False           True          False           False         False   \n",
       "245        False           True          False           False         False   \n",
       "301        False           True          False           False         False   \n",
       "311        False           True          False           False         False   \n",
       "330        False           True          False           False         False   \n",
       "333        False           True          False           False         False   \n",
       "392        False           True          False           False         False   \n",
       "436        False           True          False           False         False   \n",
       "437        False           True          False           False         False   \n",
       "448        False           True          False           False         False   \n",
       "469        False           True          False           False         False   \n",
       "513        False          False          False           False          True   \n",
       "529        False           True          False           False         False   \n",
       "565        False           True          False           False         False   \n",
       "571        False           True          False           False         False   \n",
       "600        False           True          False           False         False   \n",
       "615        False          False          False           False          True   \n",
       "618        False           True          False           False         False   \n",
       "644        False           True          False           False         False   \n",
       "655        False           True          False           False         False   \n",
       "660        False           True          False           False         False   \n",
       "665        False           True          False           False         False   \n",
       "742        False           True          False           False         False   \n",
       "754        False          False          False           False          True   \n",
       "860        False           True          False           False         False   \n",
       "\n",
       "     Cabin_Outlier  Embarked_Outlier  \n",
       "38           False             False  \n",
       "48           False             False  \n",
       "54           False             False  \n",
       "69           False             False  \n",
       "92           False             False  \n",
       "97           False             False  \n",
       "104          False             False  \n",
       "120          False             False  \n",
       "147          False             False  \n",
       "155          False             False  \n",
       "183          False             False  \n",
       "245          False             False  \n",
       "301          False             False  \n",
       "311          False             False  \n",
       "330          False             False  \n",
       "333          False             False  \n",
       "392          False             False  \n",
       "436          False             False  \n",
       "437          False             False  \n",
       "448          False             False  \n",
       "469          False             False  \n",
       "513          False             False  \n",
       "529          False             False  \n",
       "565          False             False  \n",
       "571          False             False  \n",
       "600          False             False  \n",
       "615          False             False  \n",
       "618          False             False  \n",
       "644          False             False  \n",
       "655          False             False  \n",
       "660          False             False  \n",
       "665          False             False  \n",
       "742          False             False  \n",
       "754          False             False  \n",
       "860          False             False  \n",
       "\n",
       "[35 rows x 24 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to detect outliers in a column using Z-Score\n",
    "data=df.copy()\n",
    "def detect_outliers_zscore(column, threshold=3):\n",
    " mean = np.mean(column)\n",
    " std_dev = np.std(column)\n",
    " z_scores = (column - mean) / std_dev\n",
    " # Return a boolean series where True indicates an outlier\n",
    " return np.abs(z_scores) > threshold\n",
    "# Apply the Z-Score method to each feature in the DataFrame\n",
    "for col in data.columns:\n",
    " data[f'{col}_Outlier'] = detect_outliers_zscore(data[col])\n",
    "# Filter the DataFrame to display rows where any outlier flag is True\n",
    "outliers_only = data[data.filter(like='_Outlier').any(axis=1)]\n",
    "outliers_only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "369082ec-2a23-41f7-805e-418df86e2ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Cabin                  0\n",
       "Embarked               0\n",
       "PassengerId_Outlier    0\n",
       "Survived_Outlier       0\n",
       "Pclass_Outlier         0\n",
       "Name_Outlier           0\n",
       "Sex_Outlier            0\n",
       "Age_Outlier            0\n",
       "SibSp_Outlier          0\n",
       "Parch_Outlier          0\n",
       "Ticket_Outlier         0\n",
       "Fare_Outlier           0\n",
       "Cabin_Outlier          0\n",
       "Embarked_Outlier       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "imputer = KNNImputer(n_neighbors=2)\n",
    "data_imputed_knn = pd.DataFrame(imputer.fit_transform(data), columns=data.columns)\n",
    "data_imputed_knn.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "90685eac-0a8b-4a7e-83dc-68374e953c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ad2c0fba-f9a0-438e-abf5-88123d9c7e47",
   "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>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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Titanic-Dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "32cfddb8-87e5-45af-82d9-c72705906ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.countplot(data=data, x='Sex', hue='Survived')\n",
    "plt.title('Survival Count by Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(title='Survived', loc='upper right', labels=['Not Survived', 'Survived'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "12adf486-d2de-4482-9e7f-3c0369e19ce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "correlation_matrix = data.corr()\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ba94f3d0-e27a-47c0-adac-eb203e1a02ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.histplot(data['Age'].dropna(), bins=30, kde=True)\n",
    "plt.title('Age Distribution of Passengers')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "078df01e-64ac-4dc4-b933-214e3da5f811",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
