From 7e361c2023fc5a1dda14e22106fbb81f83c75679 Mon Sep 17 00:00:00 2001
From: kamesh1106 <33192697+kamesh1106@users.noreply.github.com>
Date: Wed, 11 Sep 2019 19:14:13 +0530
Subject: [PATCH] Add files via upload

---
 Titanic.ipynb | 2601 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 2601 insertions(+)
 create mode 100644 Titanic.ipynb

diff --git a/Titanic.ipynb b/Titanic.ipynb
new file mode 100644
index 0000000..93cc007
--- /dev/null
+++ b/Titanic.ipynb
@@ -0,0 +1,2601 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Importing required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reading the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = pd.read_csv('train.csv')\n",
+    "test = pd.read_csv('test.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pId = test['PassengerId']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>886</th>\n",
+       "      <td>887</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Montvila, Rev. Juozas</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>211536</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>888</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Graham, Miss. Margaret Edith</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112053</td>\n",
+       "      <td>30.00</td>\n",
+       "      <td>B42</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>889</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>W./C. 6607</td>\n",
+       "      <td>23.45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>890</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Behr, Mr. Karl Howell</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111369</td>\n",
+       "      <td>30.00</td>\n",
+       "      <td>C148</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>891</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dooley, Mr. Patrick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370376</td>\n",
+       "      <td>7.75</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived  Pclass                                      Name  \\\n",
+       "886          887         0       2                     Montvila, Rev. Juozas   \n",
+       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
+       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
+       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
+       "890          891         0       3                       Dooley, Mr. Patrick   \n",
+       "\n",
+       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
+       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
+       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
+       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
+       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
+       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891</td>\n",
+       "      <td>891</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>204</td>\n",
+       "      <td>889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>891</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>681</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>147</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>McCoy, Mr. Bernard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CA. 2343</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C23 C25 C27</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>577</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>644</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>32.204208</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>49.693429</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.910400</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.454200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>512.329200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        PassengerId    Survived      Pclass                Name   Sex  \\\n",
+       "count    891.000000  891.000000  891.000000                 891   891   \n",
+       "unique          NaN         NaN         NaN                 891     2   \n",
+       "top             NaN         NaN         NaN  McCoy, Mr. Bernard  male   \n",
+       "freq            NaN         NaN         NaN                   1   577   \n",
+       "mean     446.000000    0.383838    2.308642                 NaN   NaN   \n",
+       "std      257.353842    0.486592    0.836071                 NaN   NaN   \n",
+       "min        1.000000    0.000000    1.000000                 NaN   NaN   \n",
+       "25%      223.500000    0.000000    2.000000                 NaN   NaN   \n",
+       "50%      446.000000    0.000000    3.000000                 NaN   NaN   \n",
+       "75%      668.500000    1.000000    3.000000                 NaN   NaN   \n",
+       "max      891.000000    1.000000    3.000000                 NaN   NaN   \n",
+       "\n",
+       "               Age       SibSp       Parch    Ticket        Fare        Cabin  \\\n",
+       "count   714.000000  891.000000  891.000000       891  891.000000          204   \n",
+       "unique         NaN         NaN         NaN       681         NaN          147   \n",
+       "top            NaN         NaN         NaN  CA. 2343         NaN  C23 C25 C27   \n",
+       "freq           NaN         NaN         NaN         7         NaN            4   \n",
+       "mean     29.699118    0.523008    0.381594       NaN   32.204208          NaN   \n",
+       "std      14.526497    1.102743    0.806057       NaN   49.693429          NaN   \n",
+       "min       0.420000    0.000000    0.000000       NaN    0.000000          NaN   \n",
+       "25%      20.125000    0.000000    0.000000       NaN    7.910400          NaN   \n",
+       "50%      28.000000    0.000000    0.000000       NaN   14.454200          NaN   \n",
+       "75%      38.000000    1.000000    0.000000       NaN   31.000000          NaN   \n",
+       "max      80.000000    8.000000    6.000000       NaN  512.329200          NaN   \n",
+       "\n",
+       "       Embarked  \n",
+       "count       889  \n",
+       "unique        3  \n",
+       "top           S  \n",
+       "freq        644  \n",
+       "mean        NaN  \n",
+       "std         NaN  \n",
+       "min         NaN  \n",
+       "25%         NaN  \n",
+       "50%         NaN  \n",
+       "75%         NaN  \n",
+       "max         NaN  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.describe(include=\"all\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
+      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(train.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>578</th>\n",
+       "      <td>579</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Caram, Mrs. Joseph (Maria Elias)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2689</td>\n",
+       "      <td>14.4583</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>407</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Widegren, Mr. Carl/Charles Peter</td>\n",
+       "      <td>male</td>\n",
+       "      <td>51.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>347064</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>832</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Richards, Master. George Sibley</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29106</td>\n",
+       "      <td>18.7500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>113</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Barton, Mr. David John</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>324669</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>838</th>\n",
+       "      <td>839</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Chip, Mr. Chang</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1601</td>\n",
+       "      <td>56.4958</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",
+       "578          579         0       3  Caram, Mrs. Joseph (Maria Elias)  female   \n",
+       "406          407         0       3  Widegren, Mr. Carl/Charles Peter    male   \n",
+       "831          832         1       2   Richards, Master. George Sibley    male   \n",
+       "112          113         0       3            Barton, Mr. David John    male   \n",
+       "838          839         1       3                   Chip, Mr. Chang    male   \n",
+       "\n",
+       "       Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \n",
+       "578    NaN      1      0    2689  14.4583   NaN        C  \n",
+       "406  51.00      0      0  347064   7.7500   NaN        S  \n",
+       "831   0.83      1      1   29106  18.7500   NaN        S  \n",
+       "112  22.00      0      0  324669   8.0500   NaN        S  \n",
+       "838  32.00      0      0    1601  56.4958   NaN        S  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>670</th>\n",
+       "      <td>671</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Brown, Mrs. Thomas William Solomon (Elizabeth ...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29750</td>\n",
+       "      <td>39.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>462</th>\n",
+       "      <td>463</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Gee, Mr. Arthur H</td>\n",
+       "      <td>male</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111320</td>\n",
+       "      <td>38.5000</td>\n",
+       "      <td>E63</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>410</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Lefebre, Miss. Ida</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4133</td>\n",
+       "      <td>25.4667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>302</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>McCoy, Mr. Bernard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>367226</td>\n",
+       "      <td>23.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>85</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Ilett, Miss. Bertha</td>\n",
+       "      <td>female</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SO/C 14885</td>\n",
+       "      <td>10.5000</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>69.5500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>492</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Windelov, Mr. Einar</td>\n",
+       "      <td>male</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SOTON/OQ 3101317</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>464</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Milling, Mr. Jacob Christian</td>\n",
+       "      <td>male</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>234360</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Smith, Mr. James Clinch</td>\n",
+       "      <td>male</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17764</td>\n",
+       "      <td>30.6958</td>\n",
+       "      <td>A7</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>719</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>McEvoy, Mr. Michael</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36568</td>\n",
+       "      <td>15.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived  Pclass  \\\n",
+       "670          671         1       2   \n",
+       "462          463         0       1   \n",
+       "409          410         0       3   \n",
+       "301          302         1       3   \n",
+       "84            85         1       2   \n",
+       "846          847         0       3   \n",
+       "491          492         0       3   \n",
+       "463          464         0       2   \n",
+       "174          175         0       1   \n",
+       "718          719         0       3   \n",
+       "\n",
+       "                                                  Name     Sex   Age  SibSp  \\\n",
+       "670  Brown, Mrs. Thomas William Solomon (Elizabeth ...  female  40.0      1   \n",
+       "462                                  Gee, Mr. Arthur H    male  47.0      0   \n",
+       "409                                 Lefebre, Miss. Ida  female   NaN      3   \n",
+       "301                                 McCoy, Mr. Bernard    male   NaN      2   \n",
+       "84                                 Ilett, Miss. Bertha  female  17.0      0   \n",
+       "846                           Sage, Mr. Douglas Bullen    male   NaN      8   \n",
+       "491                                Windelov, Mr. Einar    male  21.0      0   \n",
+       "463                       Milling, Mr. Jacob Christian    male  48.0      0   \n",
+       "174                            Smith, Mr. James Clinch    male  56.0      0   \n",
+       "718                                McEvoy, Mr. Michael    male   NaN      0   \n",
+       "\n",
+       "     Parch            Ticket     Fare Cabin Embarked  \n",
+       "670      1             29750  39.0000   NaN        S  \n",
+       "462      0            111320  38.5000   E63        S  \n",
+       "409      1              4133  25.4667   NaN        S  \n",
+       "301      0            367226  23.2500   NaN        Q  \n",
+       "84       0        SO/C 14885  10.5000   NaN        S  \n",
+       "846      2          CA. 2343  69.5500   NaN        S  \n",
+       "491      0  SOTON/OQ 3101317   7.2500   NaN        S  \n",
+       "463      0            234360  13.0000   NaN        S  \n",
+       "174      0             17764  30.6958    A7        C  \n",
+       "718      0             36568  15.5000   NaN        Q  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.sample(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(891, 12)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "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",
+      "PassengerId    891 non-null int64\n",
+      "Survived       891 non-null int64\n",
+      "Pclass         891 non-null int64\n",
+      "Name           891 non-null object\n",
+      "Sex            891 non-null object\n",
+      "Age            714 non-null float64\n",
+      "SibSp          891 non-null int64\n",
+      "Parch          891 non-null int64\n",
+      "Ticket         891 non-null object\n",
+      "Fare           891 non-null float64\n",
+      "Cabin          204 non-null object\n",
+      "Embarked       889 non-null object\n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 66.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Survived         0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age            177\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          687\n",
+       "Embarked         2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization of Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Survived')"
+      ]
+     },
+     "execution_count": 14,
+     "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"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=\"Sex\", y=\"Survived\", data=train)\n",
+    "plt.title('Survival plot based on Sex')\n",
+    "plt.xlabel('Sex')\n",
+    "plt.ylabel('Survived')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xe70ac30>"
+      ]
+     },
+     "execution_count": 15,
+     "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"
+    }
+   ],
+   "source": [
+    "sns.barplot(x=\"Pclass\", y=\"Survived\", data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xe75f990>"
+      ]
+     },
+     "execution_count": 16,
+     "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"
+    }
+   ],
+   "source": [
+    "sns.swarmplot(x=\"Age\", y=\"Survived\", data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xe879150>"
+      ]
+     },
+     "execution_count": 17,
+     "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"
+    }
+   ],
+   "source": [
+    "sns.swarmplot(x=\"Sex\", y=\"Survived\", data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>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": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891</td>\n",
+       "      <td>891</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>204</td>\n",
+       "      <td>889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>891</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>681</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>147</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>McCoy, Mr. Bernard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>CA. 2343</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C23 C25 C27</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>577</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "      <td>644</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>32.204208</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>49.693429</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.910400</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.454200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>512.329200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        PassengerId    Survived      Pclass                Name   Sex  \\\n",
+       "count    891.000000  891.000000  891.000000                 891   891   \n",
+       "unique          NaN         NaN         NaN                 891     2   \n",
+       "top             NaN         NaN         NaN  McCoy, Mr. Bernard  male   \n",
+       "freq            NaN         NaN         NaN                   1   577   \n",
+       "mean     446.000000    0.383838    2.308642                 NaN   NaN   \n",
+       "std      257.353842    0.486592    0.836071                 NaN   NaN   \n",
+       "min        1.000000    0.000000    1.000000                 NaN   NaN   \n",
+       "25%      223.500000    0.000000    2.000000                 NaN   NaN   \n",
+       "50%      446.000000    0.000000    3.000000                 NaN   NaN   \n",
+       "75%      668.500000    1.000000    3.000000                 NaN   NaN   \n",
+       "max      891.000000    1.000000    3.000000                 NaN   NaN   \n",
+       "\n",
+       "               Age       SibSp       Parch    Ticket        Fare        Cabin  \\\n",
+       "count   714.000000  891.000000  891.000000       891  891.000000          204   \n",
+       "unique         NaN         NaN         NaN       681         NaN          147   \n",
+       "top            NaN         NaN         NaN  CA. 2343         NaN  C23 C25 C27   \n",
+       "freq           NaN         NaN         NaN         7         NaN            4   \n",
+       "mean     29.699118    0.523008    0.381594       NaN   32.204208          NaN   \n",
+       "std      14.526497    1.102743    0.806057       NaN   49.693429          NaN   \n",
+       "min       0.420000    0.000000    0.000000       NaN    0.000000          NaN   \n",
+       "25%      20.125000    0.000000    0.000000       NaN    7.910400          NaN   \n",
+       "50%      28.000000    0.000000    0.000000       NaN   14.454200          NaN   \n",
+       "75%      38.000000    1.000000    0.000000       NaN   31.000000          NaN   \n",
+       "max      80.000000    8.000000    6.000000       NaN  512.329200          NaN   \n",
+       "\n",
+       "       Embarked  \n",
+       "count       889  \n",
+       "unique        3  \n",
+       "top           S  \n",
+       "freq        644  \n",
+       "mean        NaN  \n",
+       "std         NaN  \n",
+       "min         NaN  \n",
+       "25%         NaN  \n",
+       "50%         NaN  \n",
+       "75%         NaN  \n",
+       "max         NaN  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.describe(include=\"all\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0xf968e50>"
+      ]
+     },
+     "execution_count": 20,
+     "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"
+    }
+   ],
+   "source": [
+    "sns.swarmplot(x=\"Sex\", y=\"Age\", data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\hp\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xfdf5850>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.factorplot(x=\"Age\", y=\"Survived\", data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x105e2070>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.factorplot(x=\"Sex\", y=\"Survived\", data=train, kind=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0xe7ede10>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 430.5x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.factorplot(\"Pclass\",\"Survived\",\"Sex\",data=train, king=\"bar\",palette=\"muted\",legend=\"False\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x106898f0>"
+      ]
+     },
+     "execution_count": 24,
+     "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"
+    }
+   ],
+   "source": [
+    "sns.swarmplot(x=\"Pclass\", y=\"Survived\", data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x106c2470>"
+      ]
+     },
+     "execution_count": 25,
+     "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"
+    }
+   ],
+   "source": [
+    "sns.swarmplot(x=\"Pclass\", y=\"Age\", data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.swarmplot(x=\"Sex\", y=\"Pclass\", data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(train.Pclass, train.Survived, color='green')\n",
+    "plt.xlabel(\"Passenger class\")\n",
+    "plt.ylabel(\"Survivied\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import missingno as mn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mn.matrix(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_test_data = [train, test]\n",
+    "\n",
+    "for dataset in train_test_data:\n",
+    "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train['Title'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
+    "                 \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n",
+    "                 \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n",
+    "for dataset in train_test_data:\n",
+    "    dataset['Title'] = dataset['Title'].map(title_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.drop('Name', axis=1, inplace=True)\n",
+    "test.drop('Name', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sex_mapping = {\"male\": 0, \"female\": 1}\n",
+    "for dataset in train_test_data:\n",
+    "    dataset['Sex'] = dataset['Sex'].map(sex_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n",
+    "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Pclass1 = train[train['Pclass']==1]['Embarked'].value_counts()\n",
+    "Pclass2 = train[train['Pclass']==2]['Embarked'].value_counts()\n",
+    "Pclass3 = train[train['Pclass']==3]['Embarked'].value_counts()\n",
+    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
+    "df.index = ['1st class','2nd class', '3rd class']\n",
+    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for dataset in train_test_data:\n",
+    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
+    "for dataset in train_test_data:\n",
+    "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fill missing Fare with median fare for each Pclass\n",
+    "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
+    "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for dataset in train_test_data:\n",
+    "    dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0,\n",
+    "    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,\n",
+    "    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,\n",
+    "    dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for dataset in train_test_data:\n",
+    "    dataset['Cabin'] = dataset['Cabin'].str[:1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()\n",
+    "Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()\n",
+    "Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()\n",
+    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
+    "df.index = ['1st class','2nd class', '3rd class']\n",
+    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cabin_mapping = {\"A\": 0, \"B\": 0.4, \"C\": 0.8, \"D\": 1.2, \"E\": 1.6, \"F\": 2, \"G\": 2.4, \"T\": 2.8}\n",
+    "for dataset in train_test_data:\n",
+    "    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fill missing Fare with median fare for each Pclass\n",
+    "train[\"Cabin\"].fillna(train.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)\n",
+    "test[\"Cabin\"].fillna(test.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "train.sample(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mn.matrix(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.drop('PassengerId', axis=1, inplace=True)\n",
+    "test.drop('PassengerId', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train[\"FamilySize\"] = train[\"SibSp\"] + train[\"Parch\"] + 1\n",
+    "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}\n",
+    "for dataset in train_test_data:\n",
+    "    dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features_drop = ['Ticket', 'SibSp', 'Parch']\n",
+    "train = train.drop(features_drop, axis=1)\n",
+    "test = test.drop(features_drop, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = train.drop('Survived', axis=1)\n",
+    "target = train['Survived']\n",
+    "\n",
+    "train_data.shape, target.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modelling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = DecisionTreeClassifier()\n",
+    "scoring = 'accuracy'\n",
+    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
+    "print(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# decision tree Score\n",
+    "round(np.mean(score)*100, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = DecisionTreeClassifier()\n",
+    "clf.fit(train_data, target)\n",
+    "\n",
+    "test_data = test.copy()\n",
+    "prediction = clf.predict(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data.shape\n",
+    "train_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "submission = pd.DataFrame({\n",
+    "        \"PassengerId\": pId,\n",
+    "        \"Survived\": prediction\n",
+    "    })\n",
+    "\n",
+    "submission.to_csv('submission.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submission = pd.read_csv('submission.csv')\n",
+    "submission.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}