{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Dataset Explained\n",
    "| Variable Name | Definition                                                          \t |\n",
    "|-------------\t|---------------------------------------------------------------------\t |\n",
    "| PassengerId \t| A unique ID to each Passenger; **1-891**                               |\n",
    "| Survived    \t| A boolean variable; **1 - Survived, 0 - Dead**                         |\n",
    "| Pclass      \t| Ticket Class; **1 - 1st, 2 - 2nd, 3 - 3rd class**                      |\n",
    "| Name        \t| Passenger Name                                                      \t |\n",
    "| Sex         \t| Sex of Passenger                                                    \t |\n",
    "| Age         \t| Age in Years                                                        \t |\n",
    "| SibSp       \t|  Number of Siblings / Spouses Aboard                                \t |\n",
    "| Parch       \t| Number of parents / children aboard the titanic                     \t |\n",
    "| Ticket      \t| Ticket number                                                       \t |\n",
    "| Fare        \t| Passenger Fare                                                      \t |\n",
    "| Cabin       \t| Cabin number                                                        \t |\n",
    "| Embarked    \t| Port of Embarkation; **C - Cherbourg, Q - Queenstown, S - Southampton**|\n",
    "<br/>\n",
    "### Some Notes Regarding Dataset\n",
    "**Pclass**: A proxy for socio-economic status (SES)\n",
    "<br/>1st = Upper\n",
    "<br/>2nd = Middle\n",
    "<br/>3rd = Lower\n",
    "<br/><br/>\n",
    "**Age**: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "<br/><br/>\n",
    "**SibSp**: The dataset defines family relations in this way...\n",
    "<br/>Sibling = brother, sister, stepbrother, stepsister\n",
    "<br/>Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "<br/><br/>\n",
    "**Parch**: The dataset defines family relations in this way...\n",
    "<br/>Parent = mother, father\n",
    "<br/>Child = daughter, son, stepdaughter, stepson\n",
    "<br/>Some children travelled only with a nanny, therefore parch=0 for them.\n",
    "<br/><br/>\n",
    "<p style=\"text-align:center\">*Source: [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic/data)*</p>\n",
    "<br/><br/>\n",
    "**Let Us have a sneak peek at Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data and see first 5 rows\n",
    "data = pd.read_csv(\"datasets/titanic-data.csv\", index_col=[\"PassengerId\"])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Some Curious Questions\n",
    "1. Pclass as stated in notes refer to a Socio-economic status. Does it have any effect on survival?<br/><br/>\n",
    "2. Which gender survived more. In other words, does male part survived in more numbers, or does the female part. There can be many good reasons behind this question. Which are discussed ahead.<br/><br/>\n",
    "3. Which parameter have more effect over survival, sex or age? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Let's see some Information about the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      "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(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Looking at the data info it is clear that we have 891 entries. <br/><br/>\n",
    "**But there are some missing entries in Age column and quiet a lot of missing details in Cabin column.**<br/>\n",
    "**All the missing values if required will be changed to zero or will be removed from dataset as we proceed and will be noted before making the change.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<hr>\n",
    "### Tackling Question 1.\n",
    "Q. **Pclass as stated in notes refer to a Socio-economic status. Does it have any effect on survival?**\n",
    "<br/><br/>Before going ahead let's visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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rSwzVffr0qZg7d67o2LGjaN26tejUqZOYN2+eePLkiXye4qHkpb1+hw8fFj4+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\niJjO/wO8UuD51VLbJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151488ddba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new plt figure\n",
    "plt.figure(0, figsize=(6, 10))\n",
    "plt.suptitle(\"Total Passengers vs. Total Survivers vs. Total Died\", fontsize=18)\n",
    "\n",
    "# add a new subplot in first row\n",
    "# pclass vs total passengers\n",
    "plt.subplot(311)\n",
    "plt.xlabel(\"Pclass\", fontsize=13)\n",
    "plt.ylabel(\"Passengers\", fontsize=13)\n",
    "plt.hist(data['Pclass'], bins=5)\n",
    "plt.xticks([1.2, 2, 2.8], [1, 2, 3])\n",
    "\n",
    "# add a new subplot in second row\n",
    "# pclass vs total passengers survived\n",
    "plt.subplot(312)\n",
    "plt.xlabel(\"Pclass\", fontsize=13)\n",
    "plt.ylabel(\"Passengers Survived\", fontsize=13)\n",
    "plt.hist(data.loc[data['Survived'] != 0]['Pclass'], bins=5)\n",
    "plt.xticks([1.2, 2, 2.8], [1, 2, 3])\n",
    "\n",
    "# add a new subplot in 3rd row\n",
    "# pclass vs total passengers died\n",
    "plt.subplot(313)\n",
    "plt.xlabel(\"Pclass\", fontsize=13)\n",
    "plt.ylabel(\"Passengers Died\", fontsize=13)\n",
    "plt.hist(data.loc[data['Survived'] != 1]['Pclass'], bins=5)\n",
    "plt.xticks([1.2, 2, 2.8], [1, 2, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<br/>\n",
    "### Comparing The Three using Central Tendency(Mean)\n",
    "Here we will compare the Total passengers with the number of people died and Survived using mean as a probability of a person bieng survived.\n",
    "<br/><br/>\n",
    "$$\n",
    "mean(\\text{Survive | Pclass}) = \\frac{\\text{No. of People Survived from Pclass}}{\\text{Total People in Pclass}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# make a group object based on pclass\n",
    "pclass_grouped = data.groupby(\"Pclass\")\n",
    "\n",
    "# Totale Passengers of each passenger class\n",
    "total_passengers = pclass_grouped.count()[\"Survived\"]\n",
    "# No. of Survived Passengers of each passenger class\n",
    "num_survived = pclass_grouped.sum()[\"Survived\"]\n",
    "# No. of Passengers of each passenger class who died\n",
    "num_died = total_passengers - num_survived\n",
    "# Mean Survival of Each class\n",
    "mean_survival = pclass_grouped.mean()[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Died</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Total</th>\n",
       "      <th>Mean Survival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>136</td>\n",
       "      <td>216</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>87</td>\n",
       "      <td>184</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>372</td>\n",
       "      <td>119</td>\n",
       "      <td>491</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Died  Survived  Total  Mean Survival\n",
       "1    80       136    216       0.629630\n",
       "2    97        87    184       0.472826\n",
       "3   372       119    491       0.242363"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new data frame to show tabular\n",
    "# representation of the Survivals depending \n",
    "# on passenger class\n",
    "pclass_df = pd.DataFrame({\n",
    "        \"Total\": total_passengers,\n",
    "        \"Survived\": num_survived,\n",
    "        \"Died\": num_died,\n",
    "        \"Mean Survival\": mean_survival\n",
    "    },\n",
    "    index=[1, 2, 3],\n",
    "    columns=[\"Died\", \"Survived\", \"Total\", \"Mean Survival\"]\n",
    ")\n",
    "pclass_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**The number of mean survivals of 1st Passenger class (which are significantly less in number), clearly depicts the preference \n",
    "of the situation and how upper class had a better chance at survival than others.**<br/><br/>\n",
    "\n",
    "\n",
    "### Performing z-test to verify the hypothesis\n",
    "\n",
    "How to prove the hypothesis that 1st class passengers had a better chance at survival?<br/>\n",
    "Since the samples come from the population on the ship, we perform z-test to check if the upper class had better chance at survival than the population it is taken from. Our Hypothesis<br/>\n",
    "$$\n",
    "H_0: \\text{ Upper class Didn't have better chance at survival}\n",
    "$$\n",
    "<br/>\n",
    "$$\n",
    "H_1: \\text{ Upper class had better chance at survival}\n",
    "$$\n",
    "<br/>\n",
    "$$\n",
    "z = \\frac{\\text{sample mean} - \\text{Population Mean}}{\\text{Population Std / number of samples}}\n",
    "$$\n",
    "**alpha = 0.05<br/>\n",
    "It is a positive z-test to check if Chances of Survival of Upper class is greater than Chances of Survival for Population.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-score:  7.423828259\n"
     ]
    }
   ],
   "source": [
    "def z_test(sample, population):\n",
    "    sample_mean = sample.mean()\n",
    "    population_mean = population.mean()\n",
    "    std_population = population.std()\n",
    "    n = sample.shape[0]\n",
    "    den = std_population / np.sqrt(n)\n",
    "    \n",
    "    return (sample_mean - population_mean) / den\n",
    "\n",
    "print(\"Z-score: \", z_test(data[data[\"Pclass\"] ==1][\"Survived\"], data[\"Survived\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Z-score for Positive z-test with alpha level 0.05 equals 1.645.**<br/>\n",
    "**Z-score of survival of upper class (7.42) is significantly more than that of the population. This clearly indicates that upper class had a far better chance at survival.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<hr>\n",
    "### Tackling Question 2\n",
    "Q. **Which gender survived more. In other words, does male part survived in more numbers, or does the female part. There can be many good reasons behind this question.**<br/><br/>\n",
    "Reasons for putting up this question:\n",
    "1. Males' generally have more agiltiy than females'\n",
    "2. In case of a disaster male of the house tries to protect his family members, yes the other way around is possible but in a more general case the first one seems legit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's Have a sneak peek at visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Extract and Male and Female Survival Data\n",
    "male_data = data[data[\"Sex\"]==\"male\"][\"Survived\"]\n",
    "female_data = data[data[\"Sex\"]==\"female\"][\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived Female    233\n",
       "Survived Male      109\n",
       "Total Female       314\n",
       "Total Male         577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dictionary to create a Pandas Series to plot graph\n",
    "d = {\n",
    "    \"Total Male\": male_data.shape[0],\n",
    "    \"Total Female\": female_data.shape[0],\n",
    "    \"Survived Male\": male_data.sum(),\n",
    "    \"Survived Female\": female_data.sum()\n",
    "}\n",
    "\n",
    "d_keys_vals = [1, 2, 3, 4]\n",
    "se = pd.Series(d)\n",
    "se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LdP36dU2bNs3eyxQYGKhGjRrps88+05kzZ1SrVi3973//08KFC1WyZMlkE8U9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\nImJnSs5FRERERERE7Oz/Abzo18du5bqwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15148c74e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(17, 5))\n",
    "plt.suptitle(\"Gender-wise Visual Comparison of Survived and Died People\", fontsize=18)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.ylabel(\"Count\", fontsize=13)\n",
    "plt.bar(d_keys_vals, se[:])\n",
    "plt.xticks(d_keys_vals, se.index.values, fontsize=13)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.xlim(-0.2, 1.2)\n",
    "plt.scatter(np.zeros(male_data.shape), male_data, color=\"red\", s=male_data.sum()*10)\n",
    "plt.scatter(np.ones(female_data.shape), female_data, color=\"blue\", s=female_data.sum()*10)\n",
    "plt.xticks([0, 1], [\"Male\", \"Female\"], fontsize=13)\n",
    "plt.yticks([0, 1], [\"Died\", \"Survived\"], fontsize=13)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "That's seems preety unusual, for such a large number of males (approximately twice that of females) the number of survivors are less than a half compared to the number of women.<br/>\n",
    "Let's see some descriptive statistical terms over these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    577.000000\n",
       "mean       0.188908\n",
       "std        0.391775\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        0.000000\n",
       "max        1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Desciding Male Data Params\n",
    "male_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    314.000000\n",
       "mean       0.742038\n",
       "std        0.438211\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        1.000000\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describing Female Data Params\n",
    "female_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Surprising Results of Quartiles and means**\n",
    "<br/>Looking at the results, it is clear that number of males survived is around 18.9% while number of females survived is 74.3% (Remember survived = 1;died = 0, So mean will give the fraction of males and females survived which is converted to percentage above).<br/><br/>\n",
    "Looking at the 25%, 50% and 75% Quartiles, it is clear that value 1 (survived) lie in a distant corner for males. While it covers almost 50% in case of females.<br/>\n",
    "<br/>\n",
    "**The results clearly depict that a female had a more chance at survival than a male. Going by the numbers the chances were almost 4 times.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<hr>\n",
    "### Tackling Question 3\n",
    "Q. **Which parameter have more effect over survival, sex or age?**\n",
    "<br/>\n",
    "<br/>\n",
    "Surely, female to male comparison was drastic, but what about sex vs age. Does the age of a person matters more compared to the sex, may be children had a better chance, or may be aged people survived more. \n",
    "<br/>\n",
    "<br/>\n",
    "**NOTE: There are some missing values in the Age column, which are removed from dataset to form a new dataset for to answer this question.**<br/>\n",
    "Why not make them 0? This could hurt the distribution as 177 of the age values are missing and in turn can have effect on our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(714, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived   Age     Sex\n",
       "PassengerId                        \n",
       "1                   0  22.0    male\n",
       "2                   1  38.0  female\n",
       "3                   1  26.0  female\n",
       "4                   1  35.0  female\n",
       "5                   0  35.0    male"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract Age and Sex data with Survival Results\n",
    "age_vs_sex_data = data[[\"Survived\", \"Age\", \"Sex\"]]\n",
    "# remove NaN values\n",
    "age_vs_sex_data = age_vs_sex_data.dropna()\n",
    "\n",
    "print(age_vs_sex_data.shape)\n",
    "# Convert to Numpy matrix\n",
    "age_vs_sex_arr = age_vs_sex_data.as_matrix()\n",
    "# first 5 rows of data\n",
    "age_vs_sex_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let us look at the plots of the dataset to get the feel of what was the fraction of males or females and what was the median age of the passengers present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5MGDAAKUy0dHRQv/+/YWLFy8KgvC/z2VMTIxSuZCQEKV2VJSbPn26UjkvLy9BKpUKiYmJ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\nINwBAAAAgAEQ7gAAAADAAP4fa4js17TTog8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15148d64e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(15, 5))\n",
    "plt.suptitle(\"Gender-wise People Present compared to Quartile Representation of Age\", fontsize=18)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.ylabel(\"Count\", fontsize=13)\n",
    "plt.bar(d_keys_vals[2:], se[2:])\n",
    "plt.xticks(d_keys_vals[2:], se.index.values[2:], fontsize=13)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.figure(1, figsize=(8, 5))\n",
    "plt.boxplot(age_vs_sex_arr[:, 1])\n",
    "plt.xticks([1], [\"Quartile Representation\"], fontsize=13)\n",
    "plt.ylabel(\"Age\", fontsize=13)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "As we looked in question 2, the number of males were almost twice as compared to number of females. Now we can also see that the median age of the people present was around 27 or so with a few people (outlier) with age 70 and above. We can safely thus assume that there were more of young people with age between 19 to 27 and more than 50% of these people were males."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Using Scikit-learn's Logistic Regression weights\n",
    "We can use machine learning library Scikit-Learn (sklearn) to find which field have more effect. <br/>\n",
    "Logistic Regression Passes a weighted sum of the parameters to a function that gives the probability of something happening. In this case probability of Survival. The coefficient of the parameter gives us the share of the parameter in defining the probability. <br/><br/>\n",
    "In other words Logistic Regression gives more weightage to the parameter which have more effect on survival.<br/><br/>\n",
    "$$\n",
    "P(Survival) = \\text{Some Function}({w_1}{Age} + {w_2}{Sex} + w_3)\n",
    "$$\n",
    "\n",
    "<br/>\n",
    "Where,\n",
    "1. $w_1 = \\text{Coefficient of Age Parameter}$\n",
    "2. $w_2 = \\text{Coefficient of Sex Parameter}$\n",
    "3. $w_3 = \\text{Bias}$ \n",
    "<br/><br/>\n",
    "Let's see who affect survival more Age or Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,  22.,   0.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting Males to 0 and Females to 1\n",
    "# as Logistic Regression only work on numbers\n",
    "age_vs_sex_arr[age_vs_sex_arr == \"male\"] = 0\n",
    "age_vs_sex_arr[age_vs_sex_arr == \"female\"] = 1\n",
    "\n",
    "# Convert type from object to float\n",
    "age_vs_sex_arr = age_vs_sex_arr.astype(\"float64\")\n",
    "# Sanity check of to see if everything is fine\n",
    "age_vs_sex_arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes\n",
      "------------------------------\n",
      "Feature Train   | Labels\n",
      "(714, 2) \t| (714,)\n"
     ]
    }
   ],
   "source": [
    "# Divide array into Features to train on and Labels to predict\n",
    "features_train, labels_train = age_vs_sex_arr[:, 1:], age_vs_sex_arr[:, 0]\n",
    "print(\"Shapes\")\n",
    "print(\"-\"*30)\n",
    "print(\"Feature Train\", \"  |\", \"Labels\")\n",
    "print(features_train.shape, \"\\t|\", labels_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import a Logistic Regression and fit our params\n",
    "from sklearn.linear_model import LogisticRegression as LR\n",
    "clf = LR()\n",
    "clf.fit(features_train, labels_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of Age:  -0.00685726104415\n",
      "Coefficient of Sex (Female being 1 and Male being 0):  2.3689876631\n",
      "Bias:  [-1.09940297]\n",
      "--------------------------------------------------------------------------------\n",
      "Accuracy of the Classifier:  0.780112044818\n"
     ]
    }
   ],
   "source": [
    "# have a look at coefficients, Bias and Accuracy \n",
    "# on training set only\n",
    "print(\"Coefficient of Age: \", clf.coef_[0, 0])\n",
    "print(\"Coefficient of Sex (Female being 1 and Male being 0): \", clf.coef_[0, 1])\n",
    "print(\"Bias: \", clf.intercept_)\n",
    "print(\"-\"*80)\n",
    "print(\"Accuracy of the Classifier: \", clf.score(features_train, labels_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Numbers Speak**<br/>\n",
    "The Coefficient of Sex of the passenger being so high certainly speaks for itself, that how the sex majorly dominated over the survival rate compared to the age of the survivor. And the fact that it is positively correlated also speaks for being female **strongly correlated** to survival.<br/>\n",
    "Remember Correlation not equals causation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<hr>\n",
    "\n",
    "## Conclusion of Findings\n",
    "The finidings aren't purely statistically proven but the number does give us a clear idea on how the **female survival dominated male survival** and how this dominance was also over the age. By looking at data we can assume that the **affect of more male deaths must had a transferring on the age factor**, as more of the majority gender died. The other factor that really intrigued my attention was the dominance of upper class in a situation like this too.\n",
    "\n",
    "#### Limitations of Project\n",
    "The project depicts use of statistical knowledge like using z-test and predictive regression model. **But imporvements can be made like comparison of Female and Male samples as Samples from different population and then using the two sample t-test the hypothesis can be tested. The one pointed out is just an example there are a few more which can be corrected using statistical knowledge**\n",
    "\n",
    "## Future Plans\n",
    "This report covers only 3 of the main Parameters - Age, Sex and Passenger Class. There are many more degrees of freedom present in the data. For example, the following questions can still be tackled:\n",
    "1. Fare of a ticket represents the economic status of a person. (To my common sense; may not be always true). Does a person with a ticket of higher fare have higher chances of survival or not?<br/><br/>\n",
    "2. Does the deck of the cabin ensures the survival? (There will be some decks which filled with water fast as compared to others depending on the place of impact).<br/><br/>\n",
    "3. What about the title of the person in the name (Mr., Mrs. Miss, etc.), does it ensures survival?"
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