FinalTitanicData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__The Given Dataset Contains the Infomration about the Passengers Present in the Titanic . Note that while it does not contain the full Information of all the Passengers, the present dataset is large enough to correctly analyze and accurately predict the intresting question brought out by the Dataset.In this we will attempt to Answer question two of the Intresting Questions__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Questions that we will xplore in them are\n",
    "\n",
    "-   #### Which Sex is more Likely to Survive?\n",
    "     A Common question which is made all the more intresting Considering the womans and children are rescued first by lifeboat\n",
    "-   #### Depedence of Age on Survivability \n",
    "     Its intresting to Analyze how the age affects the Chances of Survival and does age have any role"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameters\n",
    "survival----->Survival\t          ( 0 = No, 1 = Yes)\n",
    "\n",
    "pclass------>\tTicket class\t   (1 = 1st, 2 = 2nd, 3 = 3rd)\n",
    "\n",
    "sex---->\tSex\t\n",
    "Age---->\tAge in years\t\n",
    "sibsp---->\t# of siblings / spouses aboard the Titanic\t\n",
    "parch----->\t# of parents / children aboard the Titanic\t\n",
    "ticket---->\tTicket number\t\n",
    "fare---->\tPassenger fare\t\n",
    "cabin---->\tCabin number\t\n",
    "embarked---->\tPort of Embarkation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RdCsv=pd.read_csv('titanic-data (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RdCsv.head(5)   #Exploring data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "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: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "RdCsv.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Dropping the Rows of Children who are below the age of 12 as they are rescued first in the Lifeboat along with the womens__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RdCsv_AgeFilter=RdCsv[RdCsv['Age']>12]  #Dropping below the age of 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why am I droping the persons with age less than 12?\n",
    "\n",
    "-  ### Apparently There is A code of Conduct dating back from 18 century that womeen and children need to be rescued first.From the WIKIPEDIA article-\n",
    "__Women and children first\" is a code of conduct dating from 1852, whereby the lives of women and children were to be saved first in a life-threatening situation, typically abandoning ship, when survival resources such as lifeboats were limited. More information about it can be found at https://en.wikipedia.org/wiki/Women_and_children_first ___\n",
    "\n",
    "- ### Within the Titanic context the same Code of conduct was applied where the Captain took the Code of conduct to a whole new level. From the PremiereExhibition Article\n",
    "__Second Officer Lightoller, who was in charge of launching the lifeboats on the port side, took the order literally, and would not allow any men in the lifeboats regardless of any vacancies available. By the time Titanic eventually sank at 2:20 a.m., all of the lifeboats had launched, and there were still some 1,500 people on board the doomed ship. Of the 2,228 passengers and crew, 531 were either women or children and they made up a staggering proportion of the 706 saved passengers demonstrating the effect of the order “women and children first. More Information can be found at http://www.premierexhibitions.com/exhibitions/3/3/titanic-artifact-exhibition/blog/truth-behind-women-and-children-first __\n",
    "\n",
    "- ### The childrens are survived regardless of sex and to minimize the effect of it in the analysis i am in support of dropping the data less than 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Total_males=len(RdCsv_AgeFilter[RdCsv_AgeFilter['Sex']=='male']) #Calculating Total Males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "416"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total_males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Total_Females=len(RdCsv_AgeFilter[RdCsv_AgeFilter['Sex']=='female'])  #Calculating females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "Total_Females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Males_Survived=len(RdCsv_AgeFilter[(RdCsv_AgeFilter['Sex']=='male') & (RdCsv_AgeFilter['Survived']==1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Female_Survived=len(RdCsv_AgeFilter[(RdCsv_AgeFilter['Sex']=='female') & (RdCsv_AgeFilter['Survived']==1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Female_Survived        #Calculating the no of Female Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Males_Survived     #Calculating the no of Male Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Ratio_Males=Males_Survived/Total_males       #Finding the ratio male Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Ratio_Females=Female_Survived/Total_Females    #Finding the ratio female Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "data": {
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "Ratio_Males"
   ]
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   "cell_type": "code",
   "execution_count": 17,
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   "outputs": [
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "Ratio_Females"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
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     "execution_count": 18,
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wWMdW1/HCNpCtERW3Z0L1TXCtjKgQIkj4qDtuO4W3LCVuylpS4gJ0PYx7/RKl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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27a71fcadd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline                     \n",
    "import matplotlib.pyplot as pit\n",
    "labels = 'Not Survived', 'Survived';\n",
    "sizes = [1-Ratio_Males, Ratio_Males]\n",
    "colors = ['Red','Green']\n",
    " \n",
    "# Plot\n",
    "\n",
    "pit.pie(sizes,labels=labels, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "pit.title('No of males Surviced out of 100 males on titanic',bbox={'pad':5})\n",
    "\n",
    "#Plotting the ratio of male Survivability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Above Plot shows the probality of no of male survivors per 100 male people in Titanic__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1caae13f160>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N7gkOY0ZMBDL2ZLJpQ6D6GJjldDFKqT4xVwrkDKeL6ElRH8h5Im7gQqDyapjj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\nyEopFSE0kJVSKkJoICulVIT4/0/1aetX8+tuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1caae0a4dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Not Survived', 'Survived';          ##Plotting the ratio of females Survivability\n",
    "sizes = [1-Ratio_Females, Ratio_Females] \n",
    "colors = ['Red','Green']\n",
    " \n",
    "# Plot\n",
    "\n",
    "pit.pie(sizes,labels=labels, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "pit.title('No of females Surviced out of 100 Females on titanic',bbox={'pad':5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Above Plot shows the probality of no of female survivors per 100 female people in Titanic__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__We Concluded that Women did have a better chance of survival as we they did Have propotionally better survivability ratio than males counterpart__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RdCsv.head(5)   #Exploring data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "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: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "RdCsv.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__We Find that 100 People didnt have age in the dataset. Hence we need to find to fill the missing Value__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Age_Survived_Table=RdCsv[['Age','Survived']]      #Grabbing the Age and Survived Coloumn of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>20.0</td>\n",
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       "      <th>13</th>\n",
       "      <td>39.0</td>\n",
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       "      <td>31.0</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>34.0</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>...</th>\n",
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       "      <th>875</th>\n",
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       "      <td>1</td>\n",
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       "      <th>876</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>877</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>881</th>\n",
       "      <td>33.0</td>\n",
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       "      <th>883</th>\n",
       "      <td>28.0</td>\n",
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       "      <th>884</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Survived\n",
       "0    22.0         0\n",
       "1    38.0         1\n",
       "2    26.0         1\n",
       "3    35.0         1\n",
       "4    35.0         0\n",
       "5     NaN         0\n",
       "6    54.0         0\n",
       "7     2.0         0\n",
       "8    27.0         1\n",
       "9    14.0         1\n",
       "10    4.0         1\n",
       "11   58.0         1\n",
       "12   20.0         0\n",
       "13   39.0         0\n",
       "14   14.0         0\n",
       "15   55.0         1\n",
       "16    2.0         0\n",
       "17    NaN         1\n",
       "18   31.0         0\n",
       "19    NaN         1\n",
       "20   35.0         0\n",
       "21   34.0         1\n",
       "22   15.0         1\n",
       "23   28.0         1\n",
       "24    8.0         0\n",
       "25   38.0         1\n",
       "26    NaN         0\n",
       "27   19.0         0\n",
       "28    NaN         1\n",
       "29    NaN         0\n",
       "..    ...       ...\n",
       "861  21.0         0\n",
       "862  48.0         1\n",
       "863   NaN         0\n",
       "864  24.0         0\n",
       "865  42.0         1\n",
       "866  27.0         1\n",
       "867  31.0         0\n",
       "868   NaN         0\n",
       "869   4.0         1\n",
       "870  26.0         0\n",
       "871  47.0         1\n",
       "872  33.0         0\n",
       "873  47.0         0\n",
       "874  28.0         1\n",
       "875  15.0         1\n",
       "876  20.0         0\n",
       "877  19.0         0\n",
       "878   NaN         0\n",
       "879  56.0         1\n",
       "880  25.0         1\n",
       "881  33.0         0\n",
       "882  22.0         0\n",
       "883  28.0         0\n",
       "884  25.0         0\n",
       "885  39.0         0\n",
       "886  27.0         0\n",
       "887  19.0         1\n",
       "888   NaN         0\n",
       "889  26.0         1\n",
       "890  32.0         0\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_Survived_Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Grabbing the Age and Survived Table from the Data Set ***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 2 columns):\n",
      "Age         714 non-null float64\n",
      "Survived    891 non-null int64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 14.0 KB\n"
     ]
    }
   ],
   "source": [
    "Age_Survived_Table.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__To Fill in the missing Value we will use the z Test to normalize the Age Data Set. Secondly we will Fill the Missing Age Value within the Standard deviation or within z score of 1 from the avaiaible dataset. Thirdly we will Convert z score back to Age to Produce the Standarized Age Table__ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Why we will Fill the missing value within one standard Deviation?__\n",
    "\n",
    "\n",
    "__By Doing it we will Fill in the Missing value with 68 percent Confidence Interval. ie The value that we are filling has the property that it will have a probality of 68 percent chance of choosing it from a seeming random sample__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Mean_Rest=Age_Survived_Table.dropna().mean()['Age']   #Calculating the Mean of data of Available Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mean_Rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Std_Rest=Age_Survived_Table.dropna().std()['Age']     # #Calculating the Standard Deviation of data of Available Data Set\n",
    "\n",
    "\n",
    "#Finding the Standard Distribution of the Avaiabaible Age Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.526497332334044"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Std_Rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.530005\n",
       "1      0.571430\n",
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       "6      1.672866\n",
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       "9     -1.080723\n",
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       "11     1.948225\n",
       "12    -0.667685\n",
       "13     0.640270\n",
       "14    -1.080723\n",
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       "17          NaN\n",
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       "26          NaN\n",
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       "28          NaN\n",
       "29          NaN\n",
       "         ...   \n",
       "861   -0.598845\n",
       "862    1.259828\n",
       "863         NaN\n",
       "864   -0.392326\n",
       "865    0.846789\n",
       "866   -0.185807\n",
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       "868         NaN\n",
       "869   -1.769120\n",
       "870   -0.254646\n",
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       "872    0.227232\n",
       "873    1.190988\n",
       "874   -0.116967\n",
       "875   -1.011883\n",
       "876   -0.667685\n",
       "877   -0.736524\n",
       "878         NaN\n",
       "879    1.810545\n",
       "880   -0.323486\n",
       "881    0.227232\n",
       "882   -0.530005\n",
       "883   -0.116967\n",
       "884   -0.323486\n",
       "885    0.640270\n",
       "886   -0.185807\n",
       "887   -0.736524\n",
       "888         NaN\n",
       "889   -0.254646\n",
       "890    0.158392\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Normalizing_out(x):                        #Normalizing the Age Table (Calculating the z score of each age value)\n",
    "   return (x-Mean_Rest)/Std_Rest\n",
    "\n",
    "Normailzed_Age=Age_Survived_Table['Age'].apply(Normalizing_out)\n",
    "Normailzed_Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yashg\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Age_Survived_Table['Normalized_Age']=Normailzed_Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Normalized_Age</th>\n",
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       "  </thead>\n",
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       "      <td>0.571430</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.672866</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.906799</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.185807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.080723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.769120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.948225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.667685</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.640270</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
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       "      <td>0</td>\n",
       "      <td>-1.080723</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.741706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.906799</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>18</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.089552</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.364911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.296072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.011883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.116967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.493761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.571430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>21.0</td>\n",
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       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.846789</td>\n",
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       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.185807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.089552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.769120</td>\n",
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       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.254646</td>\n",
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       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.190988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.227232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.190988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.116967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.011883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.667685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.736524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.810545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.323486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.227232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.530005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.116967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.323486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.640270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.185807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.736524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.254646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.158392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Survived  Normalized_Age\n",
       "0    22.0         0       -0.530005\n",
       "1    38.0         1        0.571430\n",
       "2    26.0         1       -0.254646\n",
       "3    35.0         1        0.364911\n",
       "4    35.0         0        0.364911\n",
       "5     NaN         0             NaN\n",
       "6    54.0         0        1.672866\n",
       "7     2.0         0       -1.906799\n",
       "8    27.0         1       -0.185807\n",
       "9    14.0         1       -1.080723\n",
       "10    4.0         1       -1.769120\n",
       "11   58.0         1        1.948225\n",
       "12   20.0         0       -0.667685\n",
       "13   39.0         0        0.640270\n",
       "14   14.0         0       -1.080723\n",
       "15   55.0         1        1.741706\n",
       "16    2.0         0       -1.906799\n",
       "17    NaN         1             NaN\n",
       "18   31.0         0        0.089552\n",
       "19    NaN         1             NaN\n",
       "20   35.0         0        0.364911\n",
       "21   34.0         1        0.296072\n",
       "22   15.0         1       -1.011883\n",
       "23   28.0         1       -0.116967\n",
       "24    8.0         0       -1.493761\n",
       "25   38.0         1        0.571430\n",
       "26    NaN         0             NaN\n",
       "27   19.0         0       -0.736524\n",
       "28    NaN         1             NaN\n",
       "29    NaN         0             NaN\n",
       "..    ...       ...             ...\n",
       "861  21.0         0       -0.598845\n",
       "862  48.0         1        1.259828\n",
       "863   NaN         0             NaN\n",
       "864  24.0         0       -0.392326\n",
       "865  42.0         1        0.846789\n",
       "866  27.0         1       -0.185807\n",
       "867  31.0         0        0.089552\n",
       "868   NaN         0             NaN\n",
       "869   4.0         1       -1.769120\n",
       "870  26.0         0       -0.254646\n",
       "871  47.0         1        1.190988\n",
       "872  33.0         0        0.227232\n",
       "873  47.0         0        1.190988\n",
       "874  28.0         1       -0.116967\n",
       "875  15.0         1       -1.011883\n",
       "876  20.0         0       -0.667685\n",
       "877  19.0         0       -0.736524\n",
       "878   NaN         0             NaN\n",
       "879  56.0         1        1.810545\n",
       "880  25.0         1       -0.323486\n",
       "881  33.0         0        0.227232\n",
       "882  22.0         0       -0.530005\n",
       "883  28.0         0       -0.116967\n",
       "884  25.0         0       -0.323486\n",
       "885  39.0         0        0.640270\n",
       "886  27.0         0       -0.185807\n",
       "887  19.0         1       -0.736524\n",
       "888   NaN         0             NaN\n",
       "889  26.0         1       -0.254646\n",
       "890  32.0         0        0.158392\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_Survived_Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Above table Shows the Age of each Paassenger to its corresponding z Score ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17812921221536368"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.normal(0,1)            #The Function Randomly grabs a Value within the Standard deviation of 1 Normaly Distributed\n",
    "                             # (value -1 to 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Adding_Value(x):           \n",
    "    if pd.isnull(x):\n",
    "      return np.random.normal(0,1)\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "\n",
    "Normailzed_Age_Fill=Age_Survived_Table['Normalized_Age'].apply(Adding_Value)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__  The above function fills the Missing Value from Normal Distribution with mean =0 and std=1 (Normally Distributed)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yashg\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Age_Survived_Table['Normailzed_Age_Fill']=Normailzed_Age_Fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Normalized_Age</th>\n",
       "      <th>Normailzed_Age_Fill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.530005</td>\n",
       "      <td>-0.530005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.571430</td>\n",
       "      <td>0.571430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.254646</td>\n",
       "      <td>-0.254646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.364911</td>\n",
       "      <td>0.364911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.364911</td>\n",
       "      <td>0.364911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.774637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.672866</td>\n",
       "      <td>1.672866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.906799</td>\n",
       "      <td>-1.906799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.185807</td>\n",
       "      <td>-0.185807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.080723</td>\n",
       "      <td>-1.080723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.769120</td>\n",
       "      <td>-1.769120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.948225</td>\n",
       "      <td>1.948225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.667685</td>\n",
       "      <td>-0.667685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.640270</td>\n",
       "      <td>0.640270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.080723</td>\n",
       "      <td>-1.080723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.741706</td>\n",
       "      <td>1.741706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.906799</td>\n",
       "      <td>-1.906799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.956351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.089552</td>\n",
       "      <td>0.089552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.031029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.364911</td>\n",
       "      <td>0.364911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.296072</td>\n",
       "      <td>0.296072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.011883</td>\n",
       "      <td>-1.011883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>-0.116967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.493761</td>\n",
       "      <td>-1.493761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.571430</td>\n",
       "      <td>0.571430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.910301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.736524</td>\n",
       "      <td>-0.736524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.623937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.708108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.598845</td>\n",
       "      <td>-0.598845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.259828</td>\n",
       "      <td>1.259828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.560879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.392326</td>\n",
       "      <td>-0.392326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.846789</td>\n",
       "      <td>0.846789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.185807</td>\n",
       "      <td>-0.185807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.089552</td>\n",
       "      <td>0.089552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.941693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.769120</td>\n",
       "      <td>-1.769120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.254646</td>\n",
       "      <td>-0.254646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.190988</td>\n",
       "      <td>1.190988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.227232</td>\n",
       "      <td>0.227232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.190988</td>\n",
       "      <td>1.190988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>-0.116967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.011883</td>\n",
       "      <td>-1.011883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.667685</td>\n",
       "      <td>-0.667685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.736524</td>\n",
       "      <td>-0.736524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.664864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.810545</td>\n",
       "      <td>1.810545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>-0.323486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.227232</td>\n",
       "      <td>0.227232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.530005</td>\n",
       "      <td>-0.530005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>-0.116967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>-0.323486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.640270</td>\n",
       "      <td>0.640270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.185807</td>\n",
       "      <td>-0.185807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.736524</td>\n",
       "      <td>-0.736524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.566476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.254646</td>\n",
       "      <td>-0.254646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.158392</td>\n",
       "      <td>0.158392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Survived  Normalized_Age  Normailzed_Age_Fill\n",
       "0    22.0         0       -0.530005            -0.530005\n",
       "1    38.0         1        0.571430             0.571430\n",
       "2    26.0         1       -0.254646            -0.254646\n",
       "3    35.0         1        0.364911             0.364911\n",
       "4    35.0         0        0.364911             0.364911\n",
       "5     NaN         0             NaN             1.774637\n",
       "6    54.0         0        1.672866             1.672866\n",
       "7     2.0         0       -1.906799            -1.906799\n",
       "8    27.0         1       -0.185807            -0.185807\n",
       "9    14.0         1       -1.080723            -1.080723\n",
       "10    4.0         1       -1.769120            -1.769120\n",
       "11   58.0         1        1.948225             1.948225\n",
       "12   20.0         0       -0.667685            -0.667685\n",
       "13   39.0         0        0.640270             0.640270\n",
       "14   14.0         0       -1.080723            -1.080723\n",
       "15   55.0         1        1.741706             1.741706\n",
       "16    2.0         0       -1.906799            -1.906799\n",
       "17    NaN         1             NaN             0.956351\n",
       "18   31.0         0        0.089552             0.089552\n",
       "19    NaN         1             NaN            -0.031029\n",
       "20   35.0         0        0.364911             0.364911\n",
       "21   34.0         1        0.296072             0.296072\n",
       "22   15.0         1       -1.011883            -1.011883\n",
       "23   28.0         1       -0.116967            -0.116967\n",
       "24    8.0         0       -1.493761            -1.493761\n",
       "25   38.0         1        0.571430             0.571430\n",
       "26    NaN         0             NaN            -1.910301\n",
       "27   19.0         0       -0.736524            -0.736524\n",
       "28    NaN         1             NaN             1.623937\n",
       "29    NaN         0             NaN             0.708108\n",
       "..    ...       ...             ...                  ...\n",
       "861  21.0         0       -0.598845            -0.598845\n",
       "862  48.0         1        1.259828             1.259828\n",
       "863   NaN         0             NaN             1.560879\n",
       "864  24.0         0       -0.392326            -0.392326\n",
       "865  42.0         1        0.846789             0.846789\n",
       "866  27.0         1       -0.185807            -0.185807\n",
       "867  31.0         0        0.089552             0.089552\n",
       "868   NaN         0             NaN            -0.941693\n",
       "869   4.0         1       -1.769120            -1.769120\n",
       "870  26.0         0       -0.254646            -0.254646\n",
       "871  47.0         1        1.190988             1.190988\n",
       "872  33.0         0        0.227232             0.227232\n",
       "873  47.0         0        1.190988             1.190988\n",
       "874  28.0         1       -0.116967            -0.116967\n",
       "875  15.0         1       -1.011883            -1.011883\n",
       "876  20.0         0       -0.667685            -0.667685\n",
       "877  19.0         0       -0.736524            -0.736524\n",
       "878   NaN         0             NaN            -0.664864\n",
       "879  56.0         1        1.810545             1.810545\n",
       "880  25.0         1       -0.323486            -0.323486\n",
       "881  33.0         0        0.227232             0.227232\n",
       "882  22.0         0       -0.530005            -0.530005\n",
       "883  28.0         0       -0.116967            -0.116967\n",
       "884  25.0         0       -0.323486            -0.323486\n",
       "885  39.0         0        0.640270             0.640270\n",
       "886  27.0         0       -0.185807            -0.185807\n",
       "887  19.0         1       -0.736524            -0.736524\n",
       "888   NaN         0             NaN             1.566476\n",
       "889  26.0         1       -0.254646            -0.254646\n",
       "890  32.0         0        0.158392             0.158392\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_Survived_Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** The above table Shows that Corresponding Missing age value has been filled with the normalized Value ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Corrected_Age(x):\n",
    "    return (x*Std_Rest)+Mean_Rest\n",
    "\n",
    "\n",
    "Corrected_Age=Age_Survived_Table['Normailzed_Age_Fill'].apply(Corrected_Age)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__  The above function calculates The original Age back and the age of the Random Filled Missing value from z Score__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yashg\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Age_Survived_Table['Corrected_Age']=Corrected_Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-1.769120</td>\n",
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       "      <th>11</th>\n",
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       "      <th>15</th>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34.0</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>28.0</td>\n",
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       "      <td>-0.116967</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.0</td>\n",
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       "      <td>-1.493761</td>\n",
       "      <td>-1.493761</td>\n",
       "      <td>8.000000</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.571430</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-0.371050</td>\n",
       "      <td>24.309063</td>\n",
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       "    <tr>\n",
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       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.598845</td>\n",
       "      <td>-0.598845</td>\n",
       "      <td>21.000000</td>\n",
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       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.259828</td>\n",
       "      <td>1.259828</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.128055</td>\n",
       "      <td>31.559315</td>\n",
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       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.392326</td>\n",
       "      <td>-0.392326</td>\n",
       "      <td>24.000000</td>\n",
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       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.846789</td>\n",
       "      <td>0.846789</td>\n",
       "      <td>42.000000</td>\n",
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       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.185807</td>\n",
       "      <td>-0.185807</td>\n",
       "      <td>27.000000</td>\n",
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       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.089552</td>\n",
       "      <td>0.089552</td>\n",
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       "      <td>37.858629</td>\n",
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       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.769120</td>\n",
       "      <td>-1.769120</td>\n",
       "      <td>4.000000</td>\n",
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       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.254646</td>\n",
       "      <td>-0.254646</td>\n",
       "      <td>26.000000</td>\n",
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       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.190988</td>\n",
       "      <td>1.190988</td>\n",
       "      <td>47.000000</td>\n",
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       "    <tr>\n",
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       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.227232</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.190988</td>\n",
       "      <td>1.190988</td>\n",
       "      <td>47.000000</td>\n",
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       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>28.000000</td>\n",
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       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.011883</td>\n",
       "      <td>-1.011883</td>\n",
       "      <td>15.000000</td>\n",
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       "    <tr>\n",
       "      <th>876</th>\n",
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       "      <td>-0.667685</td>\n",
       "      <td>20.000000</td>\n",
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       "    <tr>\n",
       "      <th>877</th>\n",
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       "      <td>-0.736524</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>23.329625</td>\n",
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       "      <th>879</th>\n",
       "      <td>56.0</td>\n",
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       "      <td>1.810545</td>\n",
       "      <td>1.810545</td>\n",
       "      <td>56.000000</td>\n",
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       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>25.0</td>\n",
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       "      <td>-0.323486</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>25.000000</td>\n",
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       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.227232</td>\n",
       "      <td>0.227232</td>\n",
       "      <td>33.000000</td>\n",
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       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>22.0</td>\n",
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       "      <td>-0.530005</td>\n",
       "      <td>-0.530005</td>\n",
       "      <td>22.000000</td>\n",
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       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>28.000000</td>\n",
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       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>25.000000</td>\n",
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       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.640270</td>\n",
       "      <td>0.640270</td>\n",
       "      <td>39.000000</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
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       "      <td>-0.185807</td>\n",
       "      <td>-0.185807</td>\n",
       "      <td>27.000000</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.736524</td>\n",
       "      <td>-0.736524</td>\n",
       "      <td>19.000000</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.859875</td>\n",
       "      <td>42.190094</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
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       "      <td>-0.254646</td>\n",
       "      <td>-0.254646</td>\n",
       "      <td>26.000000</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.158392</td>\n",
       "      <td>0.158392</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Survived  Normalized_Age  Normailzed_Age_Fill  Corrected_Age\n",
       "0    22.0         0       -0.530005            -0.530005      22.000000\n",
       "1    38.0         1        0.571430             0.571430      38.000000\n",
       "2    26.0         1       -0.254646            -0.254646      26.000000\n",
       "3    35.0         1        0.364911             0.364911      35.000000\n",
       "4    35.0         0        0.364911             0.364911      35.000000\n",
       "5     NaN         0             NaN            -0.672320      19.932659\n",
       "6    54.0         0        1.672866             1.672866      54.000000\n",
       "7     2.0         0       -1.906799            -1.906799       2.000000\n",
       "8    27.0         1       -0.185807            -0.185807      27.000000\n",
       "9    14.0         1       -1.080723            -1.080723      14.000000\n",
       "10    4.0         1       -1.769120            -1.769120       4.000000\n",
       "11   58.0         1        1.948225             1.948225      58.000000\n",
       "12   20.0         0       -0.667685            -0.667685      20.000000\n",
       "13   39.0         0        0.640270             0.640270      39.000000\n",
       "14   14.0         0       -1.080723            -1.080723      14.000000\n",
       "15   55.0         1        1.741706             1.741706      55.000000\n",
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       "27   19.0         0       -0.736524            -0.736524      19.000000\n",
       "28    NaN         1             NaN            -0.803282      18.030246\n",
       "29    NaN         0             NaN            -0.371050      24.309063\n",
       "..    ...       ...             ...                  ...            ...\n",
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       "869   4.0         1       -1.769120            -1.769120       4.000000\n",
       "870  26.0         0       -0.254646            -0.254646      26.000000\n",
       "871  47.0         1        1.190988             1.190988      47.000000\n",
       "872  33.0         0        0.227232             0.227232      33.000000\n",
       "873  47.0         0        1.190988             1.190988      47.000000\n",
       "874  28.0         1       -0.116967            -0.116967      28.000000\n",
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       "877  19.0         0       -0.736524            -0.736524      19.000000\n",
       "878   NaN         0             NaN            -0.438474      23.329625\n",
       "879  56.0         1        1.810545             1.810545      56.000000\n",
       "880  25.0         1       -0.323486            -0.323486      25.000000\n",
       "881  33.0         0        0.227232             0.227232      33.000000\n",
       "882  22.0         0       -0.530005            -0.530005      22.000000\n",
       "883  28.0         0       -0.116967            -0.116967      28.000000\n",
       "884  25.0         0       -0.323486            -0.323486      25.000000\n",
       "885  39.0         0        0.640270             0.640270      39.000000\n",
       "886  27.0         0       -0.185807            -0.185807      27.000000\n",
       "887  19.0         1       -0.736524            -0.736524      19.000000\n",
       "888   NaN         0             NaN             0.859875      42.190094\n",
       "889  26.0         1       -0.254646            -0.254646      26.000000\n",
       "890  32.0         0        0.158392             0.158392      32.000000\n",
       "\n",
       "[891 rows x 5 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_Survived_Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** The above Table has the addition of one column which shows each corresponding z score has Been converted back to age with the addition of new age filled value in place of Nan ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Corrected_Age_Floored=Age_Survived_Table['Corrected_Age'].apply(np.floor)\n",
    "#Since the Age cannot be negative Truncating the Decinmal Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yashg\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Age_Survived_Table['Corrected_Age_Floored']=Corrected_Age_Floored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.259828</td>\n",
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       "      <th>864</th>\n",
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       "      <td>-0.392326</td>\n",
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       "      <td>1</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.011883</td>\n",
       "      <td>-1.011883</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.667685</td>\n",
       "      <td>-0.667685</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.736524</td>\n",
       "      <td>-0.736524</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.664864</td>\n",
       "      <td>20.040971</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.810545</td>\n",
       "      <td>1.810545</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.227232</td>\n",
       "      <td>0.227232</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.530005</td>\n",
       "      <td>-0.530005</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>-0.116967</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.640270</td>\n",
       "      <td>0.640270</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.185807</td>\n",
       "      <td>-0.185807</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.736524</td>\n",
       "      <td>-0.736524</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.566476</td>\n",
       "      <td>52.454523</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.254646</td>\n",
       "      <td>-0.254646</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.158392</td>\n",
       "      <td>0.158392</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Survived  Normalized_Age  Normailzed_Age_Fill  Corrected_Age  \\\n",
       "0    22.0         0       -0.530005            -0.530005      22.000000   \n",
       "1    38.0         1        0.571430             0.571430      38.000000   \n",
       "2    26.0         1       -0.254646            -0.254646      26.000000   \n",
       "3    35.0         1        0.364911             0.364911      35.000000   \n",
       "4    35.0         0        0.364911             0.364911      35.000000   \n",
       "5     NaN         0             NaN             1.774637      55.478376   \n",
       "6    54.0         0        1.672866             1.672866      54.000000   \n",
       "7     2.0         0       -1.906799            -1.906799       2.000000   \n",
       "8    27.0         1       -0.185807            -0.185807      27.000000   \n",
       "9    14.0         1       -1.080723            -1.080723      14.000000   \n",
       "10    4.0         1       -1.769120            -1.769120       4.000000   \n",
       "11   58.0         1        1.948225             1.948225      58.000000   \n",
       "12   20.0         0       -0.667685            -0.667685      20.000000   \n",
       "13   39.0         0        0.640270             0.640270      39.000000   \n",
       "14   14.0         0       -1.080723            -1.080723      14.000000   \n",
       "15   55.0         1        1.741706             1.741706      55.000000   \n",
       "16    2.0         0       -1.906799            -1.906799       2.000000   \n",
       "17    NaN         1             NaN             0.956351      43.591549   \n",
       "18   31.0         0        0.089552             0.089552      31.000000   \n",
       "19    NaN         1             NaN            -0.031029      29.248379   \n",
       "20   35.0         0        0.364911             0.364911      35.000000   \n",
       "21   34.0         1        0.296072             0.296072      34.000000   \n",
       "22   15.0         1       -1.011883            -1.011883      15.000000   \n",
       "23   28.0         1       -0.116967            -0.116967      28.000000   \n",
       "24    8.0         0       -1.493761            -1.493761       8.000000   \n",
       "25   38.0         1        0.571430             0.571430      38.000000   \n",
       "26    NaN         0             NaN            -1.910301       1.949139   \n",
       "27   19.0         0       -0.736524            -0.736524      19.000000   \n",
       "28    NaN         1             NaN             1.623937      53.289229   \n",
       "29    NaN         0             NaN             0.708108      39.985447   \n",
       "..    ...       ...             ...                  ...            ...   \n",
       "861  21.0         0       -0.598845            -0.598845      21.000000   \n",
       "862  48.0         1        1.259828             1.259828      48.000000   \n",
       "863   NaN         0             NaN             1.560879      52.373215   \n",
       "864  24.0         0       -0.392326            -0.392326      24.000000   \n",
       "865  42.0         1        0.846789             0.846789      42.000000   \n",
       "866  27.0         1       -0.185807            -0.185807      27.000000   \n",
       "867  31.0         0        0.089552             0.089552      31.000000   \n",
       "868   NaN         0             NaN            -0.941693      16.019620   \n",
       "869   4.0         1       -1.769120            -1.769120       4.000000   \n",
       "870  26.0         0       -0.254646            -0.254646      26.000000   \n",
       "871  47.0         1        1.190988             1.190988      47.000000   \n",
       "872  33.0         0        0.227232             0.227232      33.000000   \n",
       "873  47.0         0        1.190988             1.190988      47.000000   \n",
       "874  28.0         1       -0.116967            -0.116967      28.000000   \n",
       "875  15.0         1       -1.011883            -1.011883      15.000000   \n",
       "876  20.0         0       -0.667685            -0.667685      20.000000   \n",
       "877  19.0         0       -0.736524            -0.736524      19.000000   \n",
       "878   NaN         0             NaN            -0.664864      20.040971   \n",
       "879  56.0         1        1.810545             1.810545      56.000000   \n",
       "880  25.0         1       -0.323486            -0.323486      25.000000   \n",
       "881  33.0         0        0.227232             0.227232      33.000000   \n",
       "882  22.0         0       -0.530005            -0.530005      22.000000   \n",
       "883  28.0         0       -0.116967            -0.116967      28.000000   \n",
       "884  25.0         0       -0.323486            -0.323486      25.000000   \n",
       "885  39.0         0        0.640270             0.640270      39.000000   \n",
       "886  27.0         0       -0.185807            -0.185807      27.000000   \n",
       "887  19.0         1       -0.736524            -0.736524      19.000000   \n",
       "888   NaN         0             NaN             1.566476      52.454523   \n",
       "889  26.0         1       -0.254646            -0.254646      26.000000   \n",
       "890  32.0         0        0.158392             0.158392      32.000000   \n",
       "\n",
       "     Corrected_Age_Floored  \n",
       "0                     22.0  \n",
       "1                     38.0  \n",
       "2                     26.0  \n",
       "3                     35.0  \n",
       "4                     35.0  \n",
       "5                     55.0  \n",
       "6                     54.0  \n",
       "7                      2.0  \n",
       "8                     27.0  \n",
       "9                     14.0  \n",
       "10                     4.0  \n",
       "11                    58.0  \n",
       "12                    20.0  \n",
       "13                    39.0  \n",
       "14                    14.0  \n",
       "15                    55.0  \n",
       "16                     2.0  \n",
       "17                    43.0  \n",
       "18                    31.0  \n",
       "19                    29.0  \n",
       "20                    35.0  \n",
       "21                    34.0  \n",
       "22                    15.0  \n",
       "23                    28.0  \n",
       "24                     8.0  \n",
       "25                    38.0  \n",
       "26                     1.0  \n",
       "27                    19.0  \n",
       "28                    53.0  \n",
       "29                    39.0  \n",
       "..                     ...  \n",
       "861                   21.0  \n",
       "862                   48.0  \n",
       "863                   52.0  \n",
       "864                   24.0  \n",
       "865                   42.0  \n",
       "866                   27.0  \n",
       "867                   31.0  \n",
       "868                   16.0  \n",
       "869                    4.0  \n",
       "870                   26.0  \n",
       "871                   47.0  \n",
       "872                   33.0  \n",
       "873                   47.0  \n",
       "874                   28.0  \n",
       "875                   15.0  \n",
       "876                   20.0  \n",
       "877                   19.0  \n",
       "878                   20.0  \n",
       "879                   56.0  \n",
       "880                   25.0  \n",
       "881                   33.0  \n",
       "882                   22.0  \n",
       "883                   28.0  \n",
       "884                   25.0  \n",
       "885                   39.0  \n",
       "886                   27.0  \n",
       "887                   19.0  \n",
       "888                   52.0  \n",
       "889                   26.0  \n",
       "890                   32.0  \n",
       "\n",
       "[891 rows x 6 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_Survived_Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Flooring the age values to integer since they cant be in decimal ***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Since We have Our original Table Back We can now Finally Find the Correlation__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Survived=Age_Survived_Table['Survived']\n",
    "Corrected_Age_Floor=Age_Survived_Table['Corrected_Age_Floored']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Final_Table=pd.DataFrame([Survived,Corrected_Age_Floor]).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Corrected_Age_Floored</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Died</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Survived</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Survived</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Survived</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Died</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Died</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Died</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Died</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Survived</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Survived</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Survived</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Survived</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Died</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Died</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Died</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Survived</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Died</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Survived</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Died</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Survived</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Died</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Survived</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Survived</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Survived</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Died</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Survived</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Died</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Died</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Survived</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Died</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>Died</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>Survived</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>Died</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>Died</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>Survived</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>Survived</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>Died</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>Died</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>Survived</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>Died</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>Survived</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>Died</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>Died</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>Survived</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>Survived</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>Died</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>Died</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Died</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>Survived</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>Survived</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>Died</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>Died</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>Died</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>Died</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>Died</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>Died</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Survived</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>Died</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Survived</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Died</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived Corrected_Age_Floored\n",
       "0        Died                    22\n",
       "1    Survived                    38\n",
       "2    Survived                    26\n",
       "3    Survived                    35\n",
       "4        Died                    35\n",
       "5        Died                    55\n",
       "6        Died                    54\n",
       "7        Died                     2\n",
       "8    Survived                    27\n",
       "9    Survived                    14\n",
       "10   Survived                     4\n",
       "11   Survived                    58\n",
       "12       Died                    20\n",
       "13       Died                    39\n",
       "14       Died                    14\n",
       "15   Survived                    55\n",
       "16       Died                     2\n",
       "17   Survived                    43\n",
       "18       Died                    31\n",
       "19   Survived                    29\n",
       "20       Died                    35\n",
       "21   Survived                    34\n",
       "22   Survived                    15\n",
       "23   Survived                    28\n",
       "24       Died                     8\n",
       "25   Survived                    38\n",
       "26       Died                     1\n",
       "27       Died                    19\n",
       "28   Survived                    53\n",
       "29       Died                    39\n",
       "..        ...                   ...\n",
       "861      Died                    21\n",
       "862  Survived                    48\n",
       "863      Died                    52\n",
       "864      Died                    24\n",
       "865  Survived                    42\n",
       "866  Survived                    27\n",
       "867      Died                    31\n",
       "868      Died                    16\n",
       "869  Survived                     4\n",
       "870      Died                    26\n",
       "871  Survived                    47\n",
       "872      Died                    33\n",
       "873      Died                    47\n",
       "874  Survived                    28\n",
       "875  Survived                    15\n",
       "876      Died                    20\n",
       "877      Died                    19\n",
       "878      Died                    20\n",
       "879  Survived                    56\n",
       "880  Survived                    25\n",
       "881      Died                    33\n",
       "882      Died                    22\n",
       "883      Died                    28\n",
       "884      Died                    25\n",
       "885      Died                    39\n",
       "886      Died                    27\n",
       "887  Survived                    19\n",
       "888      Died                    52\n",
       "889  Survived                    26\n",
       "890      Died                    32\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___ Prepping the Table for the Plot( Stripplot) ___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Final_Table.columns=['Status','Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Renaming the Coloumns to correctly Reflect the Axes what it will show***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Corrected_Age_Floored</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corrected_Age_Floored</th>\n",
       "      <td>-0.061308</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Survived  Corrected_Age_Floored\n",
       "Survived               1.000000              -0.061308\n",
       "Corrected_Age_Floored -0.061308               1.000000"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_Survived_Table[['Survived','Corrected_Age_Floored']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Correlation calculates the mutual relationship or connection between two or more things__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ A negative Correlation Means with increasing Age the chances of Survival is slim.  the Correlation value is small negative Value inching towards 0__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ A corrlation of -0.06 is effectively no Correlation at all and thus we cannot accuratelt predict if Chances of Survival effects with age __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yashg\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:3924: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "Age_Survived_Table['Survived'].replace({0:'Died',1:'Survived'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style='whitegrid',color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cab076f668>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9cg/VHNInN1NQXoT2AaGiy5DWHUN/9+7d2LVrF2JjY9GhQwc8/fTTcDgczVkbEemQv7e3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\nHWHoExHpCEOfiEhH/j+8p7rYaGo3YwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cab160bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x='Status',y='Age',data=Final_Table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** The above plot shows the Distribution of age vs wether the person Survived or died via stripplot***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Fron the above we can See how there is no definate Age Group in which people More or less Survived in comparision to other age group. The vertically straight line is almost equal in both the cases ***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ No definate Relation can be Understood from StripPlot which is the evidence of no correlation __"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Findings Were\n",
    "\n",
    "-   #### Female definately had a better chance of Survival than males Counterparrt\n",
    "-   #### No definte Relation Can be formed between the Depedency of survival with age (Although it indicate survival slims with \n",
    "    age)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Limitations Were\n",
    "-   #### Although Appropriate Dataset is Availaible This dataset is only a larger part of much Bigger Dataset which would have made analysis more accurate\n",
    "-   #### Lots of Missing Value particularly with age requires Stastical Methods to fill the Age which is always less accurate than the Real data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra Thoughts\n",
    "-     #### The data could have been further explored By finding the depedency of class with Survivabilty and how much Rich People survived better than Low class persons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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