{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "8b45b299",
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Ticket</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>530</td>\n",
       "      <td>2</td>\n",
       "      <td>Hocking\\t Mr. Richard George</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>466</td>\n",
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       "      <td>Goncalves\\t Mr. Manuel Estanslas</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101306</td>\n",
       "      <td>7.0500</td>\n",
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       "      <td>Vande Velde\\t Mr. Johannes Joseph</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>855</td>\n",
       "      <td>2</td>\n",
       "      <td>Carter\\t Mrs. Ernest Courtenay (Lilian Hughes)</td>\n",
       "      <td>female</td>\n",
       "      <td>44.0</td>\n",
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       "      <td>Graham\\t Mr. George Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17582</td>\n",
       "      <td>153.4625</td>\n",
       "      <td>C91</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Survived  PassengerId  Pclass  \\\n",
       "0         0          530       2   \n",
       "1         0          466       3   \n",
       "2         0          753       3   \n",
       "3         0          855       2   \n",
       "4         0          333       1   \n",
       "\n",
       "                                             Name     Sex   Age  SibSp  Parch  \\\n",
       "0                    Hocking\\t Mr. Richard George    male  23.0      2      1   \n",
       "1                Goncalves\\t Mr. Manuel Estanslas    male  38.0      0      0   \n",
       "2               Vande Velde\\t Mr. Johannes Joseph    male  33.0      0      0   \n",
       "3  Carter\\t Mrs. Ernest Courtenay (Lilian Hughes)  female  44.0      1      0   \n",
       "4                      Graham\\t Mr. George Edward    male  38.0      0      1   \n",
       "\n",
       "               Ticket      Fare Cabin Embarked  \n",
       "0               29104   11.5000   NaN        S  \n",
       "1  SOTON/O.Q. 3101306    7.0500   NaN        S  \n",
       "2              345780    9.5000   NaN        S  \n",
       "3              244252   26.0000   NaN        S  \n",
       "4            PC 17582  153.4625   C91        S  "
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "data = pd.read_csv('titanic.tsv',sep='\\t')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "8b3702f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</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",
       "      <th>Name_to_num</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>530</td>\n",
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       "      <td>S</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>855</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>244252</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17582</td>\n",
       "      <td>153.4625</td>\n",
       "      <td>C91</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  PassengerId  Pclass  Sex   Age  SibSp  Parch              Ticket  \\\n",
       "0         0          530       2    1  23.0      2      1               29104   \n",
       "1         0          466       3    1  38.0      0      0  SOTON/O.Q. 3101306   \n",
       "2         0          753       3    1  33.0      0      0              345780   \n",
       "3         0          855       2    0  44.0      1      0              244252   \n",
       "4         0          333       1    1  38.0      0      1            PC 17582   \n",
       "\n",
       "       Fare Cabin Embarked  Name_to_num  \n",
       "0   11.5000   NaN        S            1  \n",
       "1    7.0500   NaN        S            1  \n",
       "2    9.5000   NaN        S            1  \n",
       "3   26.0000   NaN        S            0  \n",
       "4  153.4625   C91        S            1  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sex'] = data['Sex'].apply(lambda x: 1 if x=='male' else 0)\n",
    "data['Name_to_num'] = data['Name'].apply(\n",
    "    lambda x: 1 if 'Mr.' in x else 0\n",
    ")\n",
    "del data['Name']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "9253cb6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Cabin'] = data['Cabin'].replace(np.nan,'Undefined')\n",
    "\n",
    "\n",
    "vectorizer = TfidfVectorizer()\n",
    "vectorizer.fit(data['Cabin'])\n",
    "vector = vectorizer.transform(data['Cabin']).toarray()\n",
    "vector_sum = []\n",
    "for v in vector:\n",
    "    vector_sum.append(v.sum())\n",
    "data['Cabin']=vector_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "0d915dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Embarked'] = data['Embarked'].replace(np.nan,'Undefined')\n",
    "\n",
    "data = pd.get_dummies(data,columns=['Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "2f641e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.00    139\n",
       "22.00     20\n",
       "21.00     19\n",
       "24.00     19\n",
       "19.00     17\n",
       "        ... \n",
       "61.00      1\n",
       "70.50      1\n",
       "0.75       1\n",
       "10.00      1\n",
       "46.00      1\n",
       "Name: Age, Length: 82, dtype: int64"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer = SimpleImputer(missing_values=np.nan, strategy='median')\n",
    "data[['Age']] = imputer.fit_transform(data[['Age']])\n",
    "data['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "536d5bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Ticket'] = data['Ticket'].replace(np.nan,'Undefined')\n",
    "\n",
    "vectorizer = TfidfVectorizer()\n",
    "vectorizer.fit(data['Ticket'])\n",
    "vector = vectorizer.transform(data['Ticket']).toarray()\n",
    "vector_sum = []\n",
    "for v in vector:\n",
    "    vector_sum.append(v.sum())\n",
    "data['Ticket']=vector_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "74e47288",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
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       "      <td>44.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.365721</td>\n",
       "      <td>153.4625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Survived  PassengerId  Pclass  Sex   Age  SibSp  Parch    Ticket      Fare  \\\n",
       "0         0          530       2    1  23.0      2      1  1.000000   11.5000   \n",
       "1         0          466       3    1  38.0      0      0  1.391284    7.0500   \n",
       "2         0          753       3    1  33.0      0      0  1.000000    9.5000   \n",
       "3         0          855       2    0  44.0      1      0  1.000000   26.0000   \n",
       "4         0          333       1    1  38.0      0      1  1.365721  153.4625   \n",
       "\n",
       "   Cabin  Name_to_num  Embarked_C  Embarked_Q  Embarked_S  Embarked_Undefined  \n",
       "0    1.0            1           0           0           1                   0  \n",
       "1    1.0            1           0           0           1                   0  \n",
       "2    1.0            1           0           0           1                   0  \n",
       "3    1.0            0           0           0           1                   0  \n",
       "4    1.0            1           0           0           1                   0  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  }
 ],
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