Exploration_and_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploratory Analysis of Training Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=pd.read_csv(\"train.csv\")\n",
    "test_df=pd.read_csv(\"test.csv\")\n",
    "gender_df=pd.read_csv(\"gender_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import utilsviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1706.67 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utilsviz.plotPerColumnDistribution(train_df,12,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utilsviz.plotCorrelationMatrix(train_df,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  }
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