{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dc0e4b87-8e9a-4411-b483-0b6f8c98013f",
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "data = pd.read_csv('titanic.csv')\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "81a637c0-ae62-4133-8b6b-e1f22b5a8c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.000000\n",
       "1      38.000000\n",
       "2      26.000000\n",
       "3      35.000000\n",
       "4      35.000000\n",
       "         ...    \n",
       "886    27.000000\n",
       "887    19.000000\n",
       "888    29.699118\n",
       "889    26.000000\n",
       "890    32.000000\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_value_age = data['Age'].mean()\n",
    "mean_value_age\n",
    "data['Age'] = data[\"Age\"].fillna(mean_value_age)\n",
    "data['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f8d1cb07-fa9e-41ec-9ea7-b1e5335359d5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>PC 17599</td>\n",
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       "      <td>29.699118</td>\n",
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       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
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       "      <td>1</td>\n",
       "      <td>Behr\\t Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>370376</td>\n",
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       "     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",
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       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                             Braund\\t Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings\\t Mrs. John Bradley (Florence Briggs T...  female  38.000000   \n",
       "2                               Heikkinen\\t Miss Laina  female  26.000000   \n",
       "3        Futrelle\\t Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                            Allen\\t Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                             Montvila\\t Rev. Juozas    male  27.000000   \n",
       "887                       Graham\\t Miss Margaret Edith  female  19.000000   \n",
       "888           Johnston\\t Miss Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                             Behr\\t Mr. Karl Howell    male  26.000000   \n",
       "890                               Dooley\\t Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Embarked  \n",
       "0        1      0         A/5 21171   7.2500        S  \n",
       "1        1      0          PC 17599  71.2833        C  \n",
       "2        0      0  STON/O2. 3101282   7.9250        S  \n",
       "3        1      0            113803  53.1000        S  \n",
       "4        0      0            373450   8.0500        S  \n",
       "..     ...    ...               ...      ...      ...  \n",
       "886      0      0            211536  13.0000        S  \n",
       "887      0      0            112053  30.0000        S  \n",
       "888      1      2        W./C. 6607  23.4500        S  \n",
       "889      0      0            111369  30.0000        C  \n",
       "890      0      0            370376   7.7500        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns=['Cabin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e237bc31-747c-44b0-a574-8c9d356756d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    1\n",
       "889    0\n",
       "890    0\n",
       "Name: Sex, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Sex\"] = data[\"Sex\"].replace({\"male\": 0, \"female\": 1})\n",
    "data[\"Sex\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b3c44a38-9cde-4bb9-8de1-702f497476e0",
   "metadata": {},
   "outputs": [
    {
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      ],
      "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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name  Sex        Age  SibSp  \\\n",
       "0                             Braund\\t Mr. Owen Harris    0  22.000000      1   \n",
       "1    Cumings\\t Mrs. John Bradley (Florence Briggs T...    1  38.000000      1   \n",
       "2                               Heikkinen\\t Miss Laina    1  26.000000      0   \n",
       "3        Futrelle\\t Mrs. Jacques Heath (Lily May Peel)    1  35.000000      1   \n",
       "4                            Allen\\t Mr. William Henry    0  35.000000      0   \n",
       "..                                                 ...  ...        ...    ...   \n",
       "886                             Montvila\\t Rev. Juozas    0  27.000000      0   \n",
       "887                       Graham\\t Miss Margaret Edith    1  19.000000      0   \n",
       "888           Johnston\\t Miss Catherine Helen \"Carrie\"    1  29.699118      1   \n",
       "889                             Behr\\t Mr. Karl Howell    0  26.000000      0   \n",
       "890                               Dooley\\t Mr. Patrick    0  32.000000      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  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dad34009-566c-46df-84c8-37f7f0f540c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 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    int64  \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  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(6), object(4)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "248e3c75-cc60-4e32-87d1-5df096f2f4a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"cleaned_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54155682-ecd3-42f6-a712-855b325f5ef1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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