Titanic - EDA.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",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ngerId</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",
       "      <th>Unnamed: 12</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",
       "      <td>NaN</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",
       "      <td>NaN</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",
       "      <td>NaN</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",
       "      <td>NaN</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ngerId  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 Unnamed: 12  \n",
       "0      0         A/5 21171   7.2500   NaN        S         NaN  \n",
       "1      0          PC 17599  71.2833   C85        C         NaN  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S         NaN  \n",
       "3      0            113803  53.1000  C123        S         NaN  \n",
       "4      0            373450   8.0500   NaN        S         NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'ngerId':'PId'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PId</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",
       "      <th>Unnamed: 12</th>\n",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   PId  Survived  Pclass                                               Name  \\\n",
       "0    1         0       3                            Braund, Mr. Owen Harris   \n",
       "1    2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "\n",
       "      Sex   Age  SibSp  Parch     Ticket     Fare Cabin Embarked Unnamed: 12  \n",
       "0    male  22.0      1      0  A/5 21171   7.2500   NaN        S         NaN  \n",
       "1  female  38.0      1      0   PC 17599  71.2833   C85        C         NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
       "       'Ticket', 'Fare', 'Cabin', 'Embarked', 'Unnamed: 12'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Unnamed: 12'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>C</td>\n",
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       "   PId  Survived  Pclass                                               Name  \\\n",
       "0    1         0       3                            Braund, Mr. Owen Harris   \n",
       "1    2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "\n",
       "      Sex   Age  SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0  A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0   PC 17599  71.2833   C85        C  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <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": [
       "              PId    Survived      Pclass         Age       SibSp       Parch  \\\n",
       "count  891.000000  891.000000  891.000000  714.000000  891.000000  891.000000   \n",
       "mean   446.000000    0.383838    2.308642   29.699118    0.523008    0.381594   \n",
       "std    257.353842    0.486592    0.836071   14.526497    1.102743    0.806057   \n",
       "min      1.000000    0.000000    1.000000    0.420000    0.000000    0.000000   \n",
       "25%    223.500000    0.000000    2.000000   20.125000    0.000000    0.000000   \n",
       "50%    446.000000    0.000000    3.000000   28.000000    0.000000    0.000000   \n",
       "75%    668.500000    1.000000    3.000000   38.000000    1.000000    0.000000   \n",
       "max    891.000000    1.000000    3.000000   80.000000    8.000000    6.000000   \n",
       "\n",
       "             Fare  \n",
       "count  891.000000  \n",
       "mean    32.204208  \n",
       "std     49.693429  \n",
       "min      0.000000  \n",
       "25%      7.910400  \n",
       "50%     14.454200  \n",
       "75%     31.000000  \n",
       "max    512.329200  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   PId       891 non-null    int64  \n",
      " 1   Survived  891 non-null    int64  \n",
      " 2   Pclass    891 non-null    int64  \n",
      " 3   Name      891 non-null    object \n",
      " 4   Sex       891 non-null    object \n",
      " 5   Age       714 non-null    float64\n",
      " 6   SibSp     891 non-null    int64  \n",
      " 7   Parch     891 non-null    int64  \n",
      " 8   Ticket    891 non-null    object \n",
      " 9   Fare      891 non-null    float64\n",
      " 10  Cabin     204 non-null    object \n",
      " 11  Embarked  61 non-null     object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PId           0\n",
       "Survived      0\n",
       "Pclass        0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Ticket        0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked    830\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isna(), yticklabels=False, cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isna(), yticklabels=False, cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This heatmap clearly shows us which particular columns/features are having NaN values.\n",
    "\n",
    "###### Here, 'Embarked' column has the highest NaN values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will visualize these 3 features with high proportion of NaN values more to get more ideas from their data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many passengers survived ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How mnay passengers survived - Class-wise ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',hue='Pclass',data=df, hue_order=[3,1,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### How many passengers survived - Sexwise ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',hue='Sex',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Age'].dropna(),kde=False,bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "4     18\n",
       "3     16\n",
       "8      7\n",
       "5      5\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SibSp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fare', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Fare'],kde=False,bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avg Age of passengers on basis of their class ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.boxplot(x='Pclass',y='Age',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above plot shows us that \n",
    "- Most of the younger passengers were travelling in 3rd class.\n",
    "- Rich passengers were in 1st class\n",
    "- Most older passengers were in 1st class \n",
    "- Hardly, there was any infant in 2nd class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling NULL values in Age column on the basis of Passengers class. This will provide better results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def age_null(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "        if Pclass == 1:\n",
    "            return 38\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 25\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = df[['Age','Pclass']].apply(age_null,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df['Age']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=df.isna(),yticklabels=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now there's no NULL values in Age Column/feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    37\n",
       "C    16\n",
       "Q     8\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
       "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
       "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
       "       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n",
       "       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n",
       "       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n",
       "       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n",
       "       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n",
       "       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n",
       "       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n",
       "       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n",
       "       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n",
       "       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n",
       "       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n",
       "       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n",
       "       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n",
       "       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n",
       "       'C148'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B96 B98        4\n",
       "C23 C25 C27    4\n",
       "G6             4\n",
       "F2             3\n",
       "E101           3\n",
       "              ..\n",
       "C111           1\n",
       "T              1\n",
       "E34            1\n",
       "C91            1\n",
       "F E69          1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are lot of NULL valuesin Cabin column.\n",
    "To make a work on these huge NULL values need to perform Feature Engineering on this column.\n",
    "\n",
    "That's why, simply dorpping it as of now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Cabin'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>PId</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>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>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>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>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>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>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PId  Survived  Pclass                                               Name  \\\n",
       "0    1         0       3                            Braund, Mr. Owen Harris   \n",
       "1    2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2    3         1       3                             Heikkinen, Miss. Laina   \n",
       "3    4         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4    5         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250        S  \n",
       "3  female  35.0      1      0            113803  53.1000        S  \n",
       "4    male  35.0      0      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(df['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "embark = pd.get_dummies(df['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Sex','Embarked','Name','PId'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</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>0</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch            Ticket     Fare\n",
       "0         0       3  22.0      1      0         A/5 21171   7.2500\n",
       "1         1       1  38.0      1      0          PC 17599  71.2833\n",
       "2         1       3  26.0      0      0  STON/O2. 3101282   7.9250\n",
       "3         1       1  35.0      1      0            113803  53.1000\n",
       "4         0       3  35.0      0      0            373450   8.0500"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch            Ticket     Fare  male  Q  S\n",
       "0         0       3  22.0      1      0         A/5 21171   7.2500     1  0  1\n",
       "1         1       1  38.0      1      0          PC 17599  71.2833     0  0  0\n",
       "2         1       3  26.0      0      0  STON/O2. 3101282   7.9250     0  0  1\n",
       "3         1       1  35.0      1      0            113803  53.1000     0  0  1\n",
       "4         0       3  35.0      0      0            373450   8.0500     1  0  1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4         0       3  35.0      0      0   8.0500     1  0  1\n",
       "5         0       3  25.0      0      0   8.4583     1  1  0\n",
       "6         0       1  54.0      0      0  51.8625     1  0  1\n",
       "7         0       3   2.0      3      1  21.0750     1  0  1\n",
       "8         1       3  27.0      0      2  11.1333     0  0  1\n",
       "9         1       2  14.0      1      0  30.0708     0  0  0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 9)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    577\n",
       "0    314\n",
       "Name: male, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['male'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "male        0\n",
       "Q           0\n",
       "S           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building Classification Model (Logistic Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "log = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 8), (268, 8))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = log.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8208955223880597\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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