titanicProject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TITANIC SHIPWRECK - Predict the Survivors!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# This allows multiple outputs per code block to display, not just the last\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The competition is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.\n",
    "\n",
    "**Goal**\n",
    "\n",
    "To predict if a passenger survived the sinking of the Titanic or not. For each passenger in the _test_ set, a prediction of a 0 or 1 value must be made for the variable.\n",
    "\n",
    "**Metric**\n",
    "\n",
    "The score is the percentage of passengers correctly predicted. This is known as accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here is the breakdown of the data types**\n",
    "\n",
    "|Variable|Definition|Key|\n",
    "|--------|----------|---|\n",
    "|survival|Survival|0 = No, 1 = Yes|\n",
    "|pclass|Ticket class|1 = 1st, 2 = 2nd, 3 = 3rd|\n",
    "|sex|Sex||\n",
    "|Age|Age in years||\n",
    "|sibsp|# of siblings / spouses aboard the Titanic||\n",
    "|parch|# of parents / children aboard the Titanic||\n",
    "|ticket|Ticket number||\n",
    "|fare|Passenger fare||\n",
    "|cabin|Cabin number||\n",
    "|embarked|Port of Embarkation|C = Cherbourg, Q = Queenstown, S = Southampton|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "    <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": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "  <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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data and take a look at it to see format and columns\n",
    "train = pd.read_csv('data/train.csv')\n",
    "test = pd.read_csv('data/test.csv')\n",
    "example_submission = pd.read_csv('data/gender_submission.csv')\n",
    "\n",
    "train.head()\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing information\n",
    "Check for any missing values and report how many there are per category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like Age and Cabin have quite a few missing values. Let's keep this in mind and investigate further later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that some variables are categorical, and perhaps not super useful in helping with classification. Let's drop these features for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0         0       3  22.0      1      0   7.2500\n",
       "1         1       1  38.0      1      0  71.2833\n",
       "2         1       3  26.0      0      0   7.9250\n",
       "3         1       1  35.0      1      0  53.1000\n",
       "4         0       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical = ['PassengerId', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']\n",
    "train.drop(categorical, axis = 1, inplace = True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore this new dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group the passengers into 0 and 1 for survived, and calculate the means for all these continuous variables to see if we can get a feel for a good predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.531876</td>\n",
       "      <td>30.626179</td>\n",
       "      <td>0.553734</td>\n",
       "      <td>0.329690</td>\n",
       "      <td>22.117887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.950292</td>\n",
       "      <td>28.343690</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.464912</td>\n",
       "      <td>48.395408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pclass        Age     SibSp     Parch       Fare\n",
       "Survived                                                    \n",
       "0         2.531876  30.626179  0.553734  0.329690  22.117887\n",
       "1         1.950292  28.343690  0.473684  0.464912  48.395408"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Survived').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like 2 variables stand out:\n",
    "\n",
    "1. Pclass\n",
    "2. Fare\n",
    "\n",
    "These 2 variables seem to have some separation for survivorship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation of all categories to survivorship\n",
    "See if there is correlation between any of the variables and survival, and sort the values to ease recognition of most positively and negatively correlated features. This backs up what we just saw above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass     -0.338481\n",
       "Age        -0.077221\n",
       "SibSp      -0.035322\n",
       "Parch       0.081629\n",
       "Fare        0.257307\n",
       "Survived    1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()['Survived'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at Age as well as Fare with respect to Survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for i in ['Age', 'Fare']:\n",
    "    died = list(train[train['Survived'] == 0][i].dropna())\n",
    "    survived = list(train[train['Survived'] == 1][i].dropna())\n",
    "    xmin = min(min(died), min(survived))\n",
    "    xmax = max(max(died), max(survived))\n",
    "    width = (xmax - xmin) / 40\n",
    "    d = sns.distplot(died, color = 'y', kde = False, bins = np.arange(xmin, xmax, width));\n",
    "    s = sns.distplot(survived, color = 'b', kde = False, bins = np.arange(xmin, xmax, width));\n",
    "    l = plt.legend(['Did not survive', 'Survived'])\n",
    "    t = plt.title('Overlaid histogram for {}'.format(i))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}