tackle_titanic_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ipython-autotime in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (0.3.1)\n",
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      "Requirement already satisfied: backcall in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from ipython->ipython-autotime) (0.2.0)\n",
      "Requirement already satisfied: setuptools>=18.5 in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from ipython->ipython-autotime) (49.2.0.post20200714)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from ipython->ipython-autotime) (2.0.10)\n",
      "Requirement already satisfied: six in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from traitlets>=4.2->ipython->ipython-autotime) (1.15.0)\n",
      "Requirement already satisfied: ipython-genutils in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from traitlets>=4.2->ipython->ipython-autotime) (0.2.0)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from pexpect; sys_platform != \"win32\"->ipython->ipython-autotime) (0.6.0)\n",
      "Requirement already satisfied: parso<0.8.0,>=0.7.0 in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from jedi>=0.10->ipython->ipython-autotime) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->ipython-autotime) (0.2.5)\n",
      "time: 449 µs (started: 2021-04-27 19:32:35 +09:00)\n"
     ]
    }
   ],
   "source": [
    "!pip install ipython-autotime\n",
    "%load_ext autotime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting kaggle\n",
      "  Downloading kaggle-1.5.12.tar.gz (58 kB)\n",
      "\u001b[K     |████████████████████████████████| 58 kB 7.8 MB/s  eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: six>=1.10 in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from kaggle) (1.15.0)\n",
      "Requirement already satisfied, skipping upgrade: certifi in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from kaggle) (2020.6.20)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from kaggle) (2.8.1)\n",
      "Requirement already satisfied, skipping upgrade: requests in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from kaggle) (2.24.0)\n",
      "Requirement already satisfied, skipping upgrade: tqdm in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from kaggle) (4.47.0)\n",
      "Collecting python-slugify\n",
      "  Downloading python-slugify-4.0.1.tar.gz (11 kB)\n",
      "Requirement already satisfied, skipping upgrade: urllib3 in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from kaggle) (1.25.9)\n",
      "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from requests->kaggle) (3.0.4)\n",
      "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /Users/aman.jain/anaconda3/lib/python3.6/site-packages (from requests->kaggle) (2.10)\n",
      "Collecting text-unidecode>=1.3\n",
      "  Downloading text_unidecode-1.3-py2.py3-none-any.whl (78 kB)\n",
      "\u001b[K     |████████████████████████████████| 78 kB 10.1 MB/s eta 0:00:01\n",
      "\u001b[?25hBuilding wheels for collected packages: kaggle, python-slugify\n",
      "  Building wheel for kaggle (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for kaggle: filename=kaggle-1.5.12-py3-none-any.whl size=73053 sha256=3102430b17c4797a01e1a6a8a6958ac02e32e54d17d4ef28e6a410fa2b7e6ec1\n",
      "  Stored in directory: /Users/aman.jain/Library/Caches/pip/wheels/77/47/e4/44a4ba1b7dfd53faaa35f59f1175e123b213ff401a8a56876b\n",
      "  Building wheel for python-slugify (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for python-slugify: filename=python_slugify-4.0.1-py2.py3-none-any.whl size=6767 sha256=81f07f95d9ee537d1ecfe0cf7bb412efe01b39f74c8b1faa47abea41b1d112c5\n",
      "  Stored in directory: /Users/aman.jain/Library/Caches/pip/wheels/72/e6/db/122611605e60148f54ee2abaca98b2bbeafc6e22486a867bad\n",
      "Successfully built kaggle python-slugify\n",
      "Installing collected packages: text-unidecode, python-slugify, kaggle\n",
      "Successfully installed kaggle-1.5.12 python-slugify-4.0.1 text-unidecode-1.3\n",
      "time: 6.14 s (started: 2021-04-27 19:44:07 +09:00)\n"
     ]
    }
   ],
   "source": [
    "!pip install kaggle --upgrade\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.1 ms (started: 2021-04-28 01:37:56 +09:00)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "train = pd.read_csv(\"./datasets/titanic/train.csv\", index_col=\"PassengerId\")\n",
    "test = pd.read_csv(\"./datasets/titanic/test.csv\", index_col=\"PassengerId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 11)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.62 ms (started: 2021-04-27 23:39:55 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n",
      "time: 6.7 ms (started: 2021-04-27 23:39:56 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <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>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>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>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>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>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>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>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>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",
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       "  </tbody>\n",
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      "text/plain": [
       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 25 ms (started: 2021-04-27 23:39:58 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>2</th>\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",
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       "    <tr>\n",
       "      <th>3</th>\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",
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       "    <tr>\n",
       "      <th>4</th>\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",
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       "    <tr>\n",
       "      <th>5</th>\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",
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      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.7 ms (started: 2021-04-27 23:40:06 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing data \n",
    "\n",
    "Age is 714/891\n",
    "\n",
    "Embarked is 204/891"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived    Pclass       Age     SibSp     Parch      Fare\n",
       "Survived  1.000000 -0.338481 -0.077221 -0.035322  0.081629  0.257307\n",
       "Pclass   -0.338481  1.000000 -0.369226  0.083081  0.018443 -0.549500\n",
       "Age      -0.077221 -0.369226  1.000000 -0.308247 -0.189119  0.096067\n",
       "SibSp    -0.035322  0.083081 -0.308247  1.000000  0.414838  0.159651\n",
       "Parch     0.081629  0.018443 -0.189119  0.414838  1.000000  0.216225\n",
       "Fare      0.257307 -0.549500  0.096067  0.159651  0.216225  1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 9.45 ms (started: 2021-04-27 23:47:03 +09:00)\n"
     ]
    }
   ],
   "source": [
    "corr = train.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.59 ms (started: 2021-04-27 23:50:03 +09:00)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcb03163748>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 99.2 ms (started: 2021-04-27 23:50:36 +09:00)\n"
     ]
    }
   ],
   "source": [
    "plt.matshow(corr, cmap=plt.cm.gray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It seems that `Parch` and `SibSp` are correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41483769862015263"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.02 ms (started: 2021-04-27 23:53:24 +09:00)\n"
     ]
    }
   ],
   "source": [
    "corr[\"Parch\"][\"SibSp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.32 ms (started: 2021-04-27 23:59:51 +09:00)\n"
     ]
    }
   ],
   "source": [
    "survived_val_counts = train[\"Survived\"].value_counts()\n",
    "survived_val_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.11 ms (started: 2021-04-28 00:01:36 +09:00)\n"
     ]
    }
   ],
   "source": [
    "survived_val_counts[:][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Passenger Count')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 111 ms (started: 2021-04-28 00:03:16 +09:00)\n"
     ]
    }
   ],
   "source": [
    "plt = train.Survived.value_counts().plot(kind='bar')\n",
    "plt.set_xlabel('Survived or not')\n",
    "plt.set_ylabel('Passenger Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for class imbalance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.99 ms (started: 2021-04-28 00:05:40 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train_columns = train.columns\n",
    "train_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 94.7 ms (started: 2021-04-28 00:07:37 +09:00)\n"
     ]
    }
   ],
   "source": [
    "plt = train.Pclass.value_counts(sort=False).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing values per columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "time: 4.66 ms (started: 2021-04-28 00:16:15 +09:00)\n"
     ]
    }
   ],
   "source": [
    "for column in train_columns:\n",
    "    print(\"{} -> {}\".format(column, train[column].isnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>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",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>3</th>\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>5</th>\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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass                            Name     Sex   Age  \\\n",
       "PassengerId                                                                   \n",
       "1                   0       3         Braund, Mr. Owen Harris    male  22.0   \n",
       "3                   1       3          Heikkinen, Miss. Laina  female  26.0   \n",
       "5                   0       3        Allen, Mr. William Henry    male  35.0   \n",
       "6                   0       3                Moran, Mr. James    male   NaN   \n",
       "8                   0       3  Palsson, Master. Gosta Leonard    male   2.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "5                0      0            373450   8.0500   NaN        S  \n",
       "6                0      0            330877   8.4583   NaN        Q  \n",
       "8                3      1            349909  21.0750   NaN        S  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 14.1 ms (started: 2021-04-28 00:50:16 +09:00)\n"
     ]
    }
   ],
   "source": [
    "some_missing_data = train[train.isnull().any(axis=1)]\n",
    "some_missing_data.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age\n",
       "PassengerId      \n",
       "1            22.0\n",
       "2            38.0\n",
       "3            26.0\n",
       "4            35.0\n",
       "5            35.0\n",
       "...           ...\n",
       "887          27.0\n",
       "888          19.0\n",
       "889           NaN\n",
       "890          26.0\n",
       "891          32.0\n",
       "\n",
       "[891 rows x 1 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 7.41 ms (started: 2021-04-28 00:54:38 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train_age = train[[\"Age\"]]\n",
    "train_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.82 ms (started: 2021-04-28 01:08:58 +09:00)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "simple_imputer = SimpleImputer(strategy=\"median\")\n",
    "\n",
    "train_age = simple_imputer.fit_transform(train_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.57 ms (started: 2021-04-28 01:04:53 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train = train.fillna({\"Embarked\": \"S\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.07 ms (started: 2021-04-28 01:07:15 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.51 ms (started: 2021-04-28 01:07:30 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train = train.drop(\"Name\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 625 µs (started: 2021-04-28 01:09:12 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train[\"Age\"] = train_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.38 ms (started: 2021-04-28 01:09:12 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train.Age.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 10 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    object \n",
      " 3   Age       891 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Ticket    891 non-null    object \n",
      " 7   Fare      891 non-null    float64\n",
      " 8   Cabin     204 non-null    object \n",
      " 9   Embarked  891 non-null    object \n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 116.6+ KB\n",
      "time: 6.02 ms (started: 2021-04-28 01:09:24 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Survived</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <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>2</th>\n",
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       "      <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>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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": [
       "             Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "PassengerId                                                                \n",
       "1                   0       3    0  22.0      1      0         A/5 21171   \n",
       "2                   1       1    1  38.0      1      0          PC 17599   \n",
       "3                   1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "4                   1       1    1  35.0      1      0            113803   \n",
       "5                   0       3    0  35.0      0      0            373450   \n",
       "\n",
       "                Fare Cabin Embarked  \n",
       "PassengerId                          \n",
       "1             7.2500   NaN        S  \n",
       "2            71.2833   C85        C  \n",
       "3             7.9250   NaN        S  \n",
       "4            53.1000  C123        S  \n",
       "5             8.0500   NaN        S  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 14.9 ms (started: 2021-04-28 01:09:37 +09:00)\n"
     ]
    }
   ],
   "source": [
    "#map each Sex value to a numerical value\n",
    "sex_mapping = {\"male\": 0, \"female\": 1}\n",
    "train['Sex'] = train['Sex'].map(sex_mapping)\n",
    "test['Sex'] = test['Sex'].map(sex_mapping)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <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>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "PassengerId                                                                \n",
       "1                   0       3    0  22.0      1      0         A/5 21171   \n",
       "2                   1       1    1  38.0      1      0          PC 17599   \n",
       "3                   1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "4                   1       1    1  35.0      1      0            113803   \n",
       "5                   0       3    0  35.0      0      0            373450   \n",
       "\n",
       "                Fare Cabin  Embarked  \n",
       "PassengerId                           \n",
       "1             7.2500   NaN         1  \n",
       "2            71.2833   C85         2  \n",
       "3             7.9250   NaN         1  \n",
       "4            53.1000  C123         1  \n",
       "5             8.0500   NaN         1  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 13.2 ms (started: 2021-04-28 01:09:49 +09:00)\n"
     ]
    }
   ],
   "source": [
    "embarked_mapping = {\"S\": 1, \"C\": 2, \"Q\": 3}\n",
    "train['Embarked'] = train['Embarked'].map(embarked_mapping)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.71 ms (started: 2021-04-28 01:10:29 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train = train.drop(['Cabin'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    int64  \n",
      " 3   Age       891 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Ticket    891 non-null    object \n",
      " 7   Fare      891 non-null    float64\n",
      " 8   Embarked  891 non-null    int64  \n",
      "dtypes: float64(2), int64(6), object(1)\n",
      "memory usage: 109.6+ KB\n",
      "time: 5.83 ms (started: 2021-04-28 01:10:40 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.45 ms (started: 2021-04-28 01:25:35 +09:00)\n"
     ]
    }
   ],
   "source": [
    "train = train.drop([\"Ticket\"], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.61 ms (started: 2021-04-28 01:25:37 +09:00)\n"
     ]
    }
   ],
   "source": [
    "X_train = train.drop([\"Survived\"], axis=1)\n",
    "y_train = train[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 7)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.76 ms (started: 2021-04-28 01:26:01 +09:00)\n"
     ]
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 585 µs (started: 2021-04-28 01:25:42 +09:00)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_clf = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 756 µs (started: 2021-04-28 01:26:09 +09:00)\n"
     ]
    }
   ],
   "source": [
    "param_grid = [\n",
    "    # try 12 (3×4) combinations of hyperparameters\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6]},\n",
    "    # then try 6 (2×3) combinations with bootstrap set as False\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 15 candidates, totalling 45 fits\n",
      "[CV] max_features=2, n_estimators=3 ..................................\n",
      "[CV] ...... max_features=2, n_estimators=3, score=0.751, total=   0.0s\n",
      "[CV] max_features=2, n_estimators=3 ..................................\n",
      "[CV] ...... max_features=2, n_estimators=3, score=0.822, total=   0.0s\n",
      "[CV] max_features=2, n_estimators=3 ..................................\n",
      "[CV] ...... max_features=2, n_estimators=3, score=0.751, total=   0.0s\n",
      "[CV] max_features=2, n_estimators=10 .................................\n",
      "[CV] ..... max_features=2, n_estimators=10, score=0.771, total=   0.0s\n",
      "[CV] max_features=2, n_estimators=10 .................................\n",
      "[CV] ..... max_features=2, n_estimators=10, score=0.811, total=   0.0s\n",
      "[CV] max_features=2, n_estimators=10 .................................\n",
      "[CV] ..... max_features=2, n_estimators=10, score=0.795, total=   0.0s\n",
      "[CV] max_features=2, n_estimators=30 .................................\n",
      "[CV] ..... max_features=2, n_estimators=30, score=0.771, total=   0.0s\n",
      "[CV] max_features=2, n_estimators=30 .................................\n",
      "[CV] ..... max_features=2, n_estimators=30, score=0.805, total=   0.0s\n",
      "[CV] max_features=2, n_estimators=30 .................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ..... max_features=2, n_estimators=30, score=0.798, total=   0.1s\n",
      "[CV] max_features=4, n_estimators=3 ..................................\n",
      "[CV] ...... max_features=4, n_estimators=3, score=0.758, total=   0.0s\n",
      "[CV] max_features=4, n_estimators=3 ..................................\n",
      "[CV] ...... max_features=4, n_estimators=3, score=0.805, total=   0.0s\n",
      "[CV] max_features=4, n_estimators=3 ..................................\n",
      "[CV] ...... max_features=4, n_estimators=3, score=0.798, total=   0.0s\n",
      "[CV] max_features=4, n_estimators=10 .................................\n",
      "[CV] ..... max_features=4, n_estimators=10, score=0.781, total=   0.0s\n",
      "[CV] max_features=4, n_estimators=10 .................................\n",
      "[CV] ..... max_features=4, n_estimators=10, score=0.815, total=   0.0s\n",
      "[CV] max_features=4, n_estimators=10 .................................\n",
      "[CV] ..... max_features=4, n_estimators=10, score=0.818, total=   0.0s\n",
      "[CV] max_features=4, n_estimators=30 .................................\n",
      "[CV] ..... max_features=4, n_estimators=30, score=0.795, total=   0.0s\n",
      "[CV] max_features=4, n_estimators=30 .................................\n",
      "[CV] ..... max_features=4, n_estimators=30, score=0.828, total=   0.1s\n",
      "[CV] max_features=4, n_estimators=30 .................................\n",
      "[CV] ..... max_features=4, n_estimators=30, score=0.798, total=   0.1s\n",
      "[CV] max_features=6, n_estimators=3 ..................................\n",
      "[CV] ...... max_features=6, n_estimators=3, score=0.788, total=   0.0s\n",
      "[CV] max_features=6, n_estimators=3 ..................................\n",
      "[CV] ...... max_features=6, n_estimators=3, score=0.808, total=   0.0s\n",
      "[CV] max_features=6, n_estimators=3 ..................................\n",
      "[CV] ...... max_features=6, n_estimators=3, score=0.754, total=   0.0s\n",
      "[CV] max_features=6, n_estimators=10 .................................\n",
      "[CV] ..... max_features=6, n_estimators=10, score=0.785, total=   0.0s\n",
      "[CV] max_features=6, n_estimators=10 .................................\n",
      "[CV] ..... max_features=6, n_estimators=10, score=0.805, total=   0.0s\n",
      "[CV] max_features=6, n_estimators=10 .................................\n",
      "[CV] ..... max_features=6, n_estimators=10, score=0.798, total=   0.0s\n",
      "[CV] max_features=6, n_estimators=30 .................................\n",
      "[CV] ..... max_features=6, n_estimators=30, score=0.795, total=   0.0s\n",
      "[CV] max_features=6, n_estimators=30 .................................\n",
      "[CV] ..... max_features=6, n_estimators=30, score=0.818, total=   0.0s\n",
      "[CV] max_features=6, n_estimators=30 .................................\n",
      "[CV] ..... max_features=6, n_estimators=30, score=0.791, total=   0.1s\n",
      "[CV] bootstrap=False, max_features=2, n_estimators=3 .................\n",
      "[CV]  bootstrap=False, max_features=2, n_estimators=3, score=0.751, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=2, n_estimators=3 .................\n",
      "[CV]  bootstrap=False, max_features=2, n_estimators=3, score=0.778, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=2, n_estimators=3 .................\n",
      "[CV]  bootstrap=False, max_features=2, n_estimators=3, score=0.754, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=2, n_estimators=10 ................\n",
      "[CV]  bootstrap=False, max_features=2, n_estimators=10, score=0.771, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=2, n_estimators=10 ................\n",
      "[CV]  bootstrap=False, max_features=2, n_estimators=10, score=0.815, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=2, n_estimators=10 ................\n",
      "[CV]  bootstrap=False, max_features=2, n_estimators=10, score=0.785, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=3, n_estimators=3 .................\n",
      "[CV]  bootstrap=False, max_features=3, n_estimators=3, score=0.761, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=3, n_estimators=3 .................\n",
      "[CV]  bootstrap=False, max_features=3, n_estimators=3, score=0.781, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=3, n_estimators=3 .................\n",
      "[CV]  bootstrap=False, max_features=3, n_estimators=3, score=0.795, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=3, n_estimators=10 ................\n",
      "[CV]  bootstrap=False, max_features=3, n_estimators=10, score=0.764, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=3, n_estimators=10 ................\n",
      "[CV]  bootstrap=False, max_features=3, n_estimators=10, score=0.788, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=3, n_estimators=10 ................\n",
      "[CV]  bootstrap=False, max_features=3, n_estimators=10, score=0.791, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=4, n_estimators=3 .................\n",
      "[CV]  bootstrap=False, max_features=4, n_estimators=3, score=0.741, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=4, n_estimators=3 .................\n",
      "[CV]  bootstrap=False, max_features=4, n_estimators=3, score=0.774, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=4, n_estimators=3 .................\n",
      "[CV]  bootstrap=False, max_features=4, n_estimators=3, score=0.791, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=4, n_estimators=10 ................\n",
      "[CV]  bootstrap=False, max_features=4, n_estimators=10, score=0.761, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=4, n_estimators=10 ................\n",
      "[CV]  bootstrap=False, max_features=4, n_estimators=10, score=0.795, total=   0.0s\n",
      "[CV] bootstrap=False, max_features=4, n_estimators=10 ................\n",
      "[CV]  bootstrap=False, max_features=4, n_estimators=10, score=0.805, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    1.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid=[{'max_features': [2, 4, 6],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.06 s (started: 2021-04-28 01:26:10 +09:00)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "grid_search  = GridSearchCV(rf_clf, param_grid, cv=3, verbose=3)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_features=4, n_estimators=30, random_state=42)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.23 ms (started: 2021-04-28 01:26:23 +09:00)\n"
     ]
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8069584736251403"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.91 ms (started: 2021-04-28 01:26:32 +09:00)\n"
     ]
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Work on test data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 892 to 1309\n",
      "Data columns (total 10 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    418 non-null    int64  \n",
      " 1   Name      418 non-null    object \n",
      " 2   Sex       418 non-null    object \n",
      " 3   Age       332 non-null    float64\n",
      " 4   SibSp     418 non-null    int64  \n",
      " 5   Parch     418 non-null    int64  \n",
      " 6   Ticket    418 non-null    object \n",
      " 7   Fare      417 non-null    float64\n",
      " 8   Cabin     91 non-null     object \n",
      " 9   Embarked  418 non-null    object \n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 35.9+ KB\n",
      "time: 6.09 ms (started: 2021-04-28 01:44:26 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.61 ms (started: 2021-04-28 01:44:26 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test = test.drop(['Cabin'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.54 ms (started: 2021-04-28 01:44:26 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test = test.drop([\"Ticket\"], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.85 ms (started: 2021-04-28 01:44:27 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test['Embarked'] = test['Embarked'].map(embarked_mapping)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.08 ms (started: 2021-04-28 01:44:27 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test = test.drop(\"Name\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 892 to 1309\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    418 non-null    int64  \n",
      " 1   Sex       418 non-null    object \n",
      " 2   Age       332 non-null    float64\n",
      " 3   SibSp     418 non-null    int64  \n",
      " 4   Parch     418 non-null    int64  \n",
      " 5   Fare      417 non-null    float64\n",
      " 6   Embarked  418 non-null    int64  \n",
      "dtypes: float64(2), int64(4), object(1)\n",
      "memory usage: 26.1+ KB\n",
      "time: 5.06 ms (started: 2021-04-28 01:44:28 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 473 µs (started: 2021-04-28 01:44:28 +09:00)\n"
     ]
    }
   ],
   "source": [
    "imputer = SimpleImputer(strategy=\"median\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.8 ms (started: 2021-04-28 01:44:30 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test.Age = imputer.fit_transform(test[[\"Age\"]])\n",
    "test.Fare = imputer.fit_transform(test[[\"Fare\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 892 to 1309\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    418 non-null    int64  \n",
      " 1   Sex       418 non-null    object \n",
      " 2   Age       418 non-null    float64\n",
      " 3   SibSp     418 non-null    int64  \n",
      " 4   Parch     418 non-null    int64  \n",
      " 5   Fare      418 non-null    float64\n",
      " 6   Embarked  418 non-null    int64  \n",
      "dtypes: float64(2), int64(4), object(1)\n",
      "memory usage: 26.1+ KB\n",
      "time: 5.46 ms (started: 2021-04-28 01:44:30 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.75 ms (started: 2021-04-28 01:44:32 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test['Sex'] = test['Sex'].map(sex_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 892 to 1309\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    418 non-null    int64  \n",
      " 1   Sex       418 non-null    int64  \n",
      " 2   Age       418 non-null    float64\n",
      " 3   SibSp     418 non-null    int64  \n",
      " 4   Parch     418 non-null    int64  \n",
      " 5   Fare      418 non-null    float64\n",
      " 6   Embarked  418 non-null    int64  \n",
      "dtypes: float64(2), int64(5)\n",
      "memory usage: 26.1 KB\n",
      "time: 5.32 ms (started: 2021-04-28 01:44:33 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.4 ms (started: 2021-04-28 01:44:40 +09:00)\n"
     ]
    }
   ],
   "source": [
    "predictions = grid_search.predict(test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       3.0000\n",
       "Sex          0.0000\n",
       "Age         27.0000\n",
       "SibSp        0.0000\n",
       "Parch        0.0000\n",
       "Fare         7.8958\n",
       "Embarked     1.0000\n",
       "Name: 902, dtype: float64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.11 ms (started: 2021-04-28 01:44:40 +09:00)\n"
     ]
    }
   ],
   "source": [
    "some_test_val = test.iloc[10]\n",
    "some_test_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.79 ms (started: 2021-04-28 01:44:42 +09:00)\n"
     ]
    }
   ],
   "source": [
    "grid_search.predict([some_test_val])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.97 ms (started: 2021-04-28 01:47:16 +09:00)\n"
     ]
    }
   ],
   "source": [
    "test_df = pd.read_csv(\"./datasets/titanic/test.csv\", index_col=\"PassengerId\")\n",
    "test_df[\"Survived\"] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.32 ms (started: 2021-04-28 01:47:23 +09:00)\n"
     ]
    }
   ],
   "source": [
    "submission = test_df[[\"Survived\"]]\n",
    "submission.to_csv(\"submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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