DataExploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "from scipy.stats import ttest_ind\n",
    "from scipy.stats import chi2_contingency\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(891, 12)"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n      dtype='object')"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n\n                                                Name     Sex   Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male  22.0      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                             Heikkinen, Miss. Laina  female  26.0      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                           Allen, Mr. William Henry    male  35.0      0   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  ",
      "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>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "PassengerId      int64\nSurvived         int64\nPclass           int64\nName            object\nSex             object\nAge            float64\nSibSp            int64\nParch            int64\nTicket          object\nFare           float64\nCabin           object\nEmbarked        object\ndtype: object"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       PassengerId    Survived      Pclass         Age       SibSp  \\\ncount   891.000000  891.000000  891.000000  714.000000  891.000000   \nmean    446.000000    0.383838    2.308642   29.699118    0.523008   \nstd     257.353842    0.486592    0.836071   14.526497    1.102743   \nmin       1.000000    0.000000    1.000000    0.420000    0.000000   \n25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n75%     668.500000    1.000000    3.000000   38.000000    1.000000   \nmax     891.000000    1.000000    3.000000   80.000000    8.000000   \n\n            Parch        Fare  \ncount  891.000000  891.000000  \nmean     0.381594   32.204208  \nstd      0.806057   49.693429  \nmin      0.000000    0.000000  \n25%      0.000000    7.910400  \n50%      0.000000   14.454200  \n75%      0.000000   31.000000  \nmax      6.000000  512.329200  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PassengerId</th>\n      <th>Survived</th>\n      <th>Pclass</th>\n      <th>Age</th>\n      <th>SibSp</th>\n      <th>Parch</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>714.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n      <td>891.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>446.000000</td>\n      <td>0.383838</td>\n      <td>2.308642</td>\n      <td>29.699118</td>\n      <td>0.523008</td>\n      <td>0.381594</td>\n      <td>32.204208</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>257.353842</td>\n      <td>0.486592</td>\n      <td>0.836071</td>\n      <td>14.526497</td>\n      <td>1.102743</td>\n      <td>0.806057</td>\n      <td>49.693429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.420000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>223.500000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>20.125000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7.910400</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>446.000000</td>\n      <td>0.000000</td>\n      <td>3.000000</td>\n      <td>28.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>14.454200</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>668.500000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>38.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>31.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>891.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>80.000000</td>\n      <td>8.000000</td>\n      <td>6.000000</td>\n      <td>512.329200</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "male      577\nfemale    314\nName: Sex, dtype: int64"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "df['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6a6243880>"
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df['Age'].hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6a635cc10>"
     },
     "metadata": {},
     "execution_count": 10
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df['Embarked'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continuous-Continuous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6a642ee50>"
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df.plot.scatter('Age','Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "           Age      Fare\nAge   1.000000  0.096067\nFare  0.096067  1.000000",
      "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>Age</th>\n      <th>Fare</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Age</th>\n      <td>1.000000</td>\n      <td>0.096067</td>\n    </tr>\n    <tr>\n      <th>Fare</th>\n      <td>0.096067</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "df[['Age', 'Fare']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical-continuous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "        count       mean        std   min   25%   50%   75%   max\nSex                                                              \nfemale  261.0  27.915709  14.110146  0.75  18.0  27.0  37.0  63.0\nmale    453.0  30.726645  14.678201  0.42  21.0  29.0  39.0  80.0",
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>Sex</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>female</th>\n      <td>261.0</td>\n      <td>27.915709</td>\n      <td>14.110146</td>\n      <td>0.75</td>\n      <td>18.0</td>\n      <td>27.0</td>\n      <td>37.0</td>\n      <td>63.0</td>\n    </tr>\n    <tr>\n      <th>male</th>\n      <td>453.0</td>\n      <td>30.726645</td>\n      <td>14.678201</td>\n      <td>0.42</td>\n      <td>21.0</td>\n      <td>29.0</td>\n      <td>39.0</td>\n      <td>80.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "df.groupby('Sex')['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Ttest_indResult(statistic=2.499206354920835, pvalue=0.012671296797014266)"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "males = df[df['Sex'] == 'male']\n",
    "females = df[df['Sex'] == 'female']\n",
    "ttest_ind(males['Age'],females['Age'],nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6a653c370>"
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df.groupby('Sex')['Age'].describe().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical-Categorical variable analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Survived    0    1\nSex               \nfemale     81  233\nmale      468  109",
      "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>Survived</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>Sex</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>female</th>\n      <td>81</td>\n      <td>233</td>\n    </tr>\n    <tr>\n      <th>male</th>\n      <td>468</td>\n      <td>109</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "pd.crosstab(df['Sex'],df['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(260.71702016732104,\n 1.1973570627755645e-58,\n 1,\n array([[193.47474747, 120.52525253],\n        [355.52525253, 221.47474747]]))"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "chi2_contingency(pd.crosstab(df['Sex'],df['Survived']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Treating missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge            177\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          687\nEmbarked         2\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "df.isnull().sum() ##shows missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "PassengerId    0\nSurvived       0\nPclass         0\nName           0\nSex            0\nAge            0\nSibSp          0\nParch          0\nTicket         0\nFare           0\nCabin          0\nEmbarked       0\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# droping all the rows where there are missing values\n",
    "newdf=df.dropna()\n",
    "newdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     PassengerId  Survived  Pclass  \\\n0              1         0       3   \n1              2         1       1   \n2              3         1       3   \n3              4         1       1   \n4              5         0       3   \n..           ...       ...     ...   \n886          887         0       2   \n887          888         1       1   \n888          889         0       3   \n889          890         1       1   \n890          891         0       3   \n\n                                                  Name     Sex   Age  SibSp  \\\n0                              Braund, Mr. Owen Harris    male  22.0      1   \n1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                               Heikkinen, Miss. Laina  female  26.0      0   \n3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                             Allen, Mr. William Henry    male  35.0      0   \n..                                                 ...     ...   ...    ...   \n886                              Montvila, Rev. Juozas    male  27.0      0   \n887                       Graham, Miss. Margaret Edith  female  19.0      0   \n888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n889                              Behr, Mr. Karl Howell    male  26.0      0   \n890                                Dooley, Mr. Patrick    male  32.0      0   \n\n     Parch            Ticket     Fare Cabin Embarked  \n0        0         A/5 21171   7.2500   NaN        S  \n1        0          PC 17599  71.2833   C85        C  \n2        0  STON/O2. 3101282   7.9250   NaN        S  \n3        0            113803  53.1000  C123        S  \n4        0            373450   8.0500   NaN        S  \n..     ...               ...      ...   ...      ...  \n886      0            211536  13.0000   NaN        S  \n887      0            112053  30.0000   B42        S  \n888      2        W./C. 6607  23.4500   NaN        S  \n889      0            111369  30.0000  C148        C  \n890      0            370376   7.7500   NaN        Q  \n\n[891 rows x 12 columns]",
      "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    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>886</th>\n      <td>887</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Montvila, Rev. Juozas</td>\n      <td>male</td>\n      <td>27.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>211536</td>\n      <td>13.0000</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>887</th>\n      <td>888</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Graham, Miss. Margaret Edith</td>\n      <td>female</td>\n      <td>19.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>112053</td>\n      <td>30.0000</td>\n      <td>B42</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td>889</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n      <td>female</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>2</td>\n      <td>W./C. 6607</td>\n      <td>23.4500</td>\n      <td>NaN</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td>890</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Behr, Mr. Karl Howell</td>\n      <td>male</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>111369</td>\n      <td>30.0000</td>\n      <td>C148</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td>891</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Dooley, Mr. Patrick</td>\n      <td>male</td>\n      <td>32.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>370376</td>\n      <td>7.7500</td>\n      <td>NaN</td>\n      <td>Q</td>\n    </tr>\n  </tbody>\n</table>\n<p>891 rows × 12 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "# droping those rows where all the entries are missing\n",
    "df.dropna(how = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     PassengerId  Survived  Pclass  \\\n0              1         0       3   \n1              2         1       1   \n2              3         1       3   \n3              4         1       1   \n4              5         0       3   \n..           ...       ...     ...   \n886          887         0       2   \n887          888         1       1   \n888          889         0       3   \n889          890         1       1   \n890          891         0       3   \n\n                                                  Name     Sex  SibSp  Parch  \\\n0                              Braund, Mr. Owen Harris    male      1      0   \n1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female      1      0   \n2                               Heikkinen, Miss. Laina  female      0      0   \n3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female      1      0   \n4                             Allen, Mr. William Henry    male      0      0   \n..                                                 ...     ...    ...    ...   \n886                              Montvila, Rev. Juozas    male      0      0   \n887                       Graham, Miss. Margaret Edith  female      0      0   \n888           Johnston, Miss. Catherine Helen \"Carrie\"  female      1      2   \n889                              Behr, Mr. Karl Howell    male      0      0   \n890                                Dooley, Mr. Patrick    male      0      0   \n\n               Ticket     Fare  \n0           A/5 21171   7.2500  \n1            PC 17599  71.2833  \n2    STON/O2. 3101282   7.9250  \n3              113803  53.1000  \n4              373450   8.0500  \n..                ...      ...  \n886            211536  13.0000  \n887            112053  30.0000  \n888        W./C. 6607  23.4500  \n889            111369  30.0000  \n890            370376   7.7500  \n\n[891 rows x 9 columns]",
      "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>SibSp</th>\n      <th>Parch</th>\n      <th>Ticket</th>\n      <th>Fare</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>1</td>\n      <td>0</td>\n      <td>A/5 21171</td>\n      <td>7.2500</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>1</td>\n      <td>0</td>\n      <td>PC 17599</td>\n      <td>71.2833</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>0</td>\n      <td>0</td>\n      <td>STON/O2. 3101282</td>\n      <td>7.9250</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>1</td>\n      <td>0</td>\n      <td>113803</td>\n      <td>53.1000</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>0</td>\n      <td>0</td>\n      <td>373450</td>\n      <td>8.0500</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>886</th>\n      <td>887</td>\n      <td>0</td>\n      <td>2</td>\n      <td>Montvila, Rev. Juozas</td>\n      <td>male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>211536</td>\n      <td>13.0000</td>\n    </tr>\n    <tr>\n      <th>887</th>\n      <td>888</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Graham, Miss. Margaret Edith</td>\n      <td>female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>112053</td>\n      <td>30.0000</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td>889</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n      <td>female</td>\n      <td>1</td>\n      <td>2</td>\n      <td>W./C. 6607</td>\n      <td>23.4500</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td>890</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Behr, Mr. Karl Howell</td>\n      <td>male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>111369</td>\n      <td>30.0000</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td>891</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Dooley, Mr. Patrick</td>\n      <td>male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>370376</td>\n      <td>7.7500</td>\n    </tr>\n  </tbody>\n</table>\n<p>891 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "# Droping columns where there is any missing value\n",
    "df.dropna(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## filling the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0      22.0\n1      38.0\n2      26.0\n3      35.0\n4      35.0\n       ... \n886    27.0\n887    19.0\n888     NaN\n889    26.0\n890    32.0\nName: Age, Length: 891, dtype: float64"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "# filling 0 \n",
    "df['Age']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0      22.00000\n1      38.00000\n2      26.00000\n3      35.00000\n4      35.00000\n         ...   \n886    27.00000\n887    19.00000\n888    29.20404\n889    26.00000\n890    32.00000\nName: Age, Length: 888, dtype: float64"
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "df['Age'].fillna(df['Age'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Treatment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate outlier Detectin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 375.2875 248.518125\" width=\"375.2875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 248.518125 \nL 375.2875 248.518125 \nL 375.2875 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 224.64 \nL 368.0875 224.64 \nL 368.0875 7.2 \nL 33.2875 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"matplotlib.axis_1\">\n    <g id=\"xtick_1\">\n     <g id=\"line2d_1\">\n      <defs>\n       <path d=\"M 0 0 \nL 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df['Fare'].plot.box()\n",
    "plt.show()\n",
    "df['Age'].plot.box()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate outlier detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6a602ffa0>"
     },
     "metadata": {},
     "execution_count": 25
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df.plot.scatter('Age','Fare')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing outliers in Fare (>300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Fare']<300]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replacing outlier ages with mean value of ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Age']>65,'Age'] = np.mean(df['Age']) ##in loc first is row and second is col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df.plot.scatter('Age','Fare')\n",
    "plt.show()\n",
    "df['Age'].plot.box()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6a68e97f0>"
     },
     "metadata": {},
     "execution_count": 29
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df['Age'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [0,18,50,65]\n",
    "groups = ['Child','Adult','Senior citizen']\n",
    "df['age_group'] = pd.cut(df['Age'],bins,labels = groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Adult             516\nChild             139\nSenior citizen     56\nName: age_group, dtype: int64"
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "df['age_group'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.drop('Name',axis =1)\n",
    "data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop(['Survived'],axis=1)\n",
    "y = data['Survived']\n",
    "x.fillna(0,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x,test_x,train_y,test_y = train_test_split(x,y,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    0.618619\n1    0.381381\nName: Survived, dtype: float64"
     },
     "metadata": {},
     "execution_count": 88
    }
   ],
   "source": [
    "train_y.value_counts()/len(train_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    0.617117\n1    0.382883\nName: Survived, dtype: float64"
     },
     "metadata": {},
     "execution_count": 89
    }
   ],
   "source": [
    "test_y.value_counts()/len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "LogisticRegression()"
     },
     "metadata": {},
     "execution_count": 94
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "logregscore = logreg.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "DecisionTreeClassifier()"
     },
     "metadata": {},
     "execution_count": 96
    }
   ],
   "source": [
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtreescore = dtree.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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