project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "import warnings\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and check data\n",
    "\n",
    "train_df = pd.read_csv('./data/train.csv')\n",
    "test_df = pd.read_csv('./data/test.csv')\n",
    "test_PassengerId = test_df['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_Data = train_df.select_dtypes([np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\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>-0.035144</td>\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>0.036847</td>\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>-0.057527</td>\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>-0.001652</td>\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>0.012658</td>\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": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_Data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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    object \n",
      " 5   Age          714 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(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variable Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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    object \n",
      " 5   Age          714 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(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_plot(variable):\n",
    "    \n",
    "    #get feature\n",
    "    var= train_df[variable]\n",
    "    # count number of categorical variable(value/sample)\n",
    "    varValue = var.value_counts()\n",
    "    \n",
    "    #visualize\n",
    "    plt.figure(figsize=(9,3))\n",
    "    plt.bar(varValue.index,varValue)\n",
    "    plt.xticks(varValue.index,varValue.index.values)\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.title(variable)\n",
    "    plt.show()\n",
    "    print(\"{}:\\n{}\".format(variable,varValue))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived:\n",
      "Survived\n",
      "0    549\n",
      "1    342\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex:\n",
      "Sex\n",
      "male      577\n",
      "female    314\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass:\n",
      "Pclass\n",
      "3    491\n",
      "1    216\n",
      "2    184\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embarked:\n",
      "Embarked\n",
      "S    644\n",
      "C    168\n",
      "Q     77\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SibSp:\n",
      "SibSp\n",
      "0    608\n",
      "1    209\n",
      "2     28\n",
      "4     18\n",
      "3     16\n",
      "8      7\n",
      "5      5\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parch:\n",
      "Parch\n",
      "0    678\n",
      "1    118\n",
      "2     80\n",
      "5      5\n",
      "3      5\n",
      "4      4\n",
      "6      1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "category1 = [\"Survived\",\"Sex\",\"Pclass\",\"Embarked\",\"SibSp\",\"Parch\"]\n",
    "for c in category1:\n",
    "    bar_plot(c)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cabin\n",
      "B96 B98        4\n",
      "G6             4\n",
      "C23 C25 C27    4\n",
      "C22 C26        3\n",
      "F33            3\n",
      "              ..\n",
      "E34            1\n",
      "C7             1\n",
      "C54            1\n",
      "E36            1\n",
      "C148           1\n",
      "Name: count, Length: 147, dtype: int64\n",
      "\n",
      "Name\n",
      "Braund, Mr. Owen Harris                     1\n",
      "Boulos, Mr. Hanna                           1\n",
      "Frolicher-Stehli, Mr. Maxmillian            1\n",
      "Gilinski, Mr. Eliezer                       1\n",
      "Murdlin, Mr. Joseph                         1\n",
      "                                           ..\n",
      "Kelly, Miss. Anna Katherine \"Annie Kate\"    1\n",
      "McCoy, Mr. Bernard                          1\n",
      "Johnson, Mr. William Cahoone Jr             1\n",
      "Keane, Miss. Nora A                         1\n",
      "Dooley, Mr. Patrick                         1\n",
      "Name: count, Length: 891, dtype: int64\n",
      "\n",
      "Ticket\n",
      "347082      7\n",
      "CA. 2343    7\n",
      "1601        7\n",
      "3101295     6\n",
      "CA 2144     6\n",
      "           ..\n",
      "9234        1\n",
      "19988       1\n",
      "2693        1\n",
      "PC 17612    1\n",
      "370376      1\n",
      "Name: count, Length: 681, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "category2 = [\"Cabin\",\"Name\",\"Ticket\"]\n",
    "for c in category2:\n",
    "    print(\"{}\\n\".format(train_df[c].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(variable):\n",
    "    plt.figure(figsize=(9,3))\n",
    "    plt.hist(train_df[variable],bins=50)\n",
    "    plt.xlabel(variable)\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.title(\"{} distribution with hist\".format(variable))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numericVar = [\"Fare\",\"Age\",\"PassengerId\"]\n",
    "for n in numericVar:\n",
    "    plot_hist(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Pclass\",\"Survived\"]].groupby([\"Pclass\"],as_index=False).mean().sort_values(by=\"Survived\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Sex\",\"Survived\"]].groupby([\"Sex\"],as_index=False).mean().sort_values(by=\"Survived\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"SibSp\",\"Survived\"]].groupby([\"SibSp\"],as_index=False).mean().sort_values(by=\"Survived\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\",\"Survived\"]].groupby([\"Parch\"],as_index=False).mean().sort_values(by=\"Survived\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlier Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_outlier(df,features):\n",
    "    outlier_indices = []\n",
    "    \n",
    "    for c in features:\n",
    "        # 1st quartile\n",
    "        Q1=np.percentile(df[c],25)\n",
    "        # 3rd quartile\n",
    "        Q3=np.percentile(df[c],75)\n",
    "        # IQR\n",
    "        IQR = Q3-Q1\n",
    "        # Outlier step\n",
    "        outlier_step = IQR*1.5\n",
    "        # detect outlier and their indices\n",
    "        outlier_list_col = df[(df[c]<Q1-outlier_step)| (df[c]> Q3+outlier_step)].index\n",
    "        # store indices\n",
    "        outlier_indices.extend(outlier_list_col)\n",
    "    outlier_indices = Counter(outlier_indices)\n",
    "    multiple_outliers = list(i for i,v in outlier_indices.items() if v>2)\n",
    "    return multiple_outliers\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.00</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.00</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Master. Thomas Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Constance Gladys</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. George John Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Alice Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.00</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>793</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Stella Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>847</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. Douglas Bullen</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
       "27            28         0       1     Fortune, Mr. Charles Alexander    male   \n",
       "88            89         1       1         Fortune, Miss. Mabel Helen  female   \n",
       "159          160         0       3         Sage, Master. Thomas Henry    male   \n",
       "180          181         0       3       Sage, Miss. Constance Gladys  female   \n",
       "201          202         0       3                Sage, Mr. Frederick    male   \n",
       "324          325         0       3           Sage, Mr. George John Jr    male   \n",
       "341          342         1       1     Fortune, Miss. Alice Elizabeth  female   \n",
       "792          793         0       3            Sage, Miss. Stella Anna  female   \n",
       "846          847         0       3           Sage, Mr. Douglas Bullen    male   \n",
       "863          864         0       3  Sage, Miss. Dorothy Edith \"Dolly\"  female   \n",
       "\n",
       "      Age  SibSp  Parch    Ticket    Fare        Cabin Embarked  \n",
       "27   19.0      3      2     19950  263.00  C23 C25 C27        S  \n",
       "88   23.0      3      2     19950  263.00  C23 C25 C27        S  \n",
       "159   NaN      8      2  CA. 2343   69.55          NaN        S  \n",
       "180   NaN      8      2  CA. 2343   69.55          NaN        S  \n",
       "201   NaN      8      2  CA. 2343   69.55          NaN        S  \n",
       "324   NaN      8      2  CA. 2343   69.55          NaN        S  \n",
       "341  24.0      3      2     19950  263.00  C23 C25 C27        S  \n",
       "792   NaN      8      2  CA. 2343   69.55          NaN        S  \n",
       "846   NaN      8      2  CA. 2343   69.55          NaN        S  \n",
       "863   NaN      8      2  CA. 2343   69.55          NaN        S  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.loc[detect_outlier(train_df,[\"Age\",\"SibSp\",\"Parch\",\"Fare\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop outlier\n",
    "\n",
    "train_df = train_df.drop(detect_outlier(train_df,[\"Age\",\"SibSp\",\"Parch\",\"Fare\"]),axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_len = len(train_df)\n",
    "train_df = pd.concat([train_df,test_df],axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Age', 'Fare', 'Cabin', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns[train_df.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "Survived        418\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "Age             256\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              1\n",
       "Cabin          1007\n",
       "Embarked          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>62</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>830</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "60            62       1.0       1                        Icard, Miss. Amelie   \n",
       "821          830       1.0       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
       "60   female  38.0      0      0  113572  80.0   B28      NaN  \n",
       "821  female  62.0      0      0  113572  80.0   B28      NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.boxplot(column='Fare',by='Embarked')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Embarked'] = train_df['Embarked'].fillna(\"C\")\n",
    "train_df[train_df['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>1044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>male</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass                Name   Sex   Age  SibSp  \\\n",
       "1033         1044       NaN       3  Storey, Mr. Thomas  male  60.5      0   \n",
       "\n",
       "      Parch Ticket  Fare Cabin Embarked  \n",
       "1033      0   3701   NaN   NaN        S  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df['Fare'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Fare'] = train_df['Fare'].fillna(np.mean(train_df[train_df['Pclass'] == 3]['Fare']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df['Fare'].isnull()]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "datai",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
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}