Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Titanic Kaggle Competition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg.\n",
    "Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Descirption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* survival\tSurvival\t0 = No, 1 = Yes\n",
    "* pclass\tTicket class\t1 = 1st, 2 = 2nd, 3 = 3rd\n",
    "* sex\tSex\t\n",
    "* Age\tAge in years\t\n",
    "* sibsp\t# of siblings / spouses aboard the Titanic\t\n",
    "* parch\t# of parents / children aboard the Titanic\t\n",
    "* ticket\tTicket number\t\n",
    "* fare\tPassenger fare\t\n",
    "* cabin\tCabin number\t\n",
    "* embarked\tPort of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variable Notes\n",
    "* pclass: A proxy for socio-economic status (SES)\n",
    "    1st = Upper\n",
    "    2nd = Middle\n",
    "    3rd = Lower\n",
    "\n",
    "* age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "* sibsp: The dataset defines family relations in this way...\n",
    "* Sibling = brother, sister, stepbrother, stepsister\n",
    "* Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "* parch: The dataset defines family relations in this way...\n",
    "* Parent = mother, father\n",
    "* Child = daughter, son, stepdaughter, stepson\n",
    "* Some children travelled only with a nanny, therefore parch=0 for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the neccessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Train CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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   PassengerId  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     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      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         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Cabin\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
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       "      <td>C</td>\n",
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       "    <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>7.9250</td>\n",
       "      <td>S</td>\n",
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       "    <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>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>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch     Fare Embarked  \n",
       "0      0   7.2500        S  \n",
       "1      0  71.2833        C  \n",
       "2      0   7.9250        S  \n",
       "3      0  53.1000        S  \n",
       "4      0   8.0500        S  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(\"Ticket\", axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "      <td>-0.013166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.169718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>-0.164681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.032565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>-0.068900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>-0.040449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>-0.013166</td>\n",
       "      <td>0.169718</td>\n",
       "      <td>-0.164681</td>\n",
       "      <td>0.032565</td>\n",
       "      <td>-0.068900</td>\n",
       "      <td>-0.040449</td>\n",
       "      <td>0.226311</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "Embarked       -0.013166  0.169718 -0.164681  0.032565 -0.068900 -0.040449   \n",
       "\n",
       "                 Fare  Embarked  \n",
       "PassengerId  0.012658 -0.013166  \n",
       "Survived     0.257307  0.169718  \n",
       "Pclass      -0.549500 -0.164681  \n",
       "Age          0.096067  0.032565  \n",
       "SibSp        0.159651 -0.068900  \n",
       "Parch        0.216225 -0.040449  \n",
       "Fare         1.000000  0.226311  \n",
       "Embarked     0.226311  1.000000  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
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       "      <th>Title</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>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</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>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <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>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
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       "    <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>53.1000</td>\n",
       "      <td>S</td>\n",
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       "      <td>0</td>\n",
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       "      <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>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch     Fare Embarked Title  \n",
       "0      0   7.2500        S    Mr  \n",
       "1      0  71.2833        C   Mrs  \n",
       "2      0   7.9250        S  Miss  \n",
       "3      0  53.1000        S   Mrs  \n",
       "4      0   8.0500        S    Mr  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'] = df['Name'].str.extract(r' ([A-Za-z]+)\\.')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr      519\n",
       "Miss    182\n",
       "Mrs     131\n",
       "Mas      40\n",
       "Dr        7\n",
       "Rev       6\n",
       "Col       6\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Title\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>642</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sagesser, Mlle. Emma</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69.3000</td>\n",
       "      <td>C</td>\n",
       "      <td>Mlle</td>\n",
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       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>711</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mayne, Mlle. Berthe Antonine (\"Mrs de Villiers\")</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.5042</td>\n",
       "      <td>C</td>\n",
       "      <td>Mlle</td>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "641          642         1       1   \n",
       "710          711         1       1   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "641                              Sagesser, Mlle. Emma  female  24.0      0   \n",
       "710  Mayne, Mlle. Berthe Antonine (\"Mrs de Villiers\")  female  24.0      0   \n",
       "\n",
       "     Parch     Fare Embarked Title  \n",
       "641      0  69.3000        C  Mlle  \n",
       "710      0  49.5042        C  Mlle  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Title\"]==\"Mlle\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "rep = {\"Master\": \"Mas\",\"Major\":\"Col\",\"Mlle\":\"Mrs\",\"Capt\":\"Col\",\"Don\":\"Mr\",\"Lady\":\"Mrs\",\"Jonkheer\":\"Mr\",\"Mme\":\"Mrs\",\"Lady\":\"Mrs\",\"Sir\":\"Col\",\"Countess\":\"Mrs\",\"Ms\":\"Mrs\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Title\"]=df[\"Title\"].replace(\"High\",\"Mrs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Title\"] = df[\"Title\"].replace(rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</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>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>Mr</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>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch     Fare Embarked Title  \n",
       "0        0   7.2500        S    Mr  \n",
       "1        0  71.2833        C   Mrs  \n",
       "2        0   7.9250        S  Miss  \n",
       "3        0  53.1000        S   Mrs  \n",
       "4        0   8.0500        S    Mr  \n",
       "..     ...      ...      ...   ...  \n",
       "886      0  13.0000        S   Rev  \n",
       "887      0  30.0000        S  Miss  \n",
       "888      2  23.4500        S  Miss  \n",
       "889      0  30.0000        C    Mr  \n",
       "890      0   7.7500        Q    Mr  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df[\"Survived\"],df[\"Embarked\"]).T.plot(kind=\"bar\", figsize=(10,8),color=[\"salmon\",\"LightGreen\"],rot=0)\n",
    "plt.title(\"Survival vs Embarked Station\")\n",
    "plt.legend([\"Didn't Survive\",\"Survived\"])\n",
    "plt.xlabel(\"Embarked Station\")\n",
    "plt.ylabel(\"No of passengers\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Embarked\"] = df[\"Embarked\"].replace({\"S\":1,\"Q\":2,\"C\":3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17c84eca748>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,8))\n",
    "sns.heatmap(corr, annot=True, cmap=\"YlGnBu\", linewidth = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17c85017d30>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Survived, df.Sex).T.plot(kind=\"bar\", figsize=(10,5), color=[\"Salmon\", \"LightGreen\"],rot=0)\n",
    "plt.legend([\"Didn't Survive\", \"Survived\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AWf3WKUkarmlDoao+tm48yU7AOfT+ev8C8LHplpMkvXT1fZ9Ckl2B9wCn0bun4JCqemwUhUmSRq/fOYU/BU4BlgKvqaqnR1aVJGks+l199F7gXwC/D6xJ8mT7PJWk3/0GkqSXqH7nFHzXgiRtZfyHX5LUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSZ2hhUKSi5I8kuTOSW27Jrk2yb1tuEtrT5Lzk6xKcnuSQ4ZVlyRpesPcU/gs8Ib12s4Frquq/YHr2jTA8cD+7bME+PQQ65IkTWNooVBVXwN+sF7zScCyNr4MOHlS++eq5yZgbpL5w6pNkjS1UZ9T2LOqHgJowz1a+17Ag5P6rW5tG0iyJMmKJCvWrl071GIlaWuzpZxozhRtNVXHqlpaVRNVNTFv3rwhlyVJW5dRh8LD6w4LteEjrX01sPekfguANSOuTZK2eqMOheXAoja+CLhqUvvp7SqkI4An1h1mkiSNzpxhrTjJXwFHA7snWQ38AXAecFmSxcADwKmt+1eAE4BVwLPAGcOqS5I0vaGFQlW9dZpZx03Rt4CzhlWLJGkwW8qJZknSFsBQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUmeLCoUkb0hyT5JVSc4ddz2StLXZYkIhybbAp4DjgVcDb03y6vFWJUlbly0mFIDDgFVVdV9V/Qj4AnDSmGuSpK3KnHEXMMlewIOTplcDh6/fKckSYEmbfDrJPSOobWuxO/DouIuQpuBv88X1L6ebsSWFQqZoqw0aqpYCS4dfztYnyYqqmhh3HdL6/G2OzpZ0+Gg1sPek6QXAmjHVIklbpS0pFL4J7J9knyTbA28Blo+5Jknaqmwxh4+q6vkk7wKuAbYFLqqqu8Zc1tbGw3LaUvnbHJFUbXDYXpK0ldqSDh9JksbMUJAkdQwFTSnJ0UmuHncdmh2SnJ3k7iSXDGn9f5jkfcNY99ZmiznRLGlWeydwfFXdP+5C1J97CrNYkoVJvpPkgiR3Jrkkya8k+fsk9yY5rH3+IcktbXjAFOvZMclFSb7Z+vn4EQ0syf8E9gWWJ/m9qX5LSd6e5K+TfDnJ/UneleQ9rc9NSXZt/d7Rlr0tyRVJXj7F9n4uyd8kWZnk60l+frTf+KXNUJj99gM+CfwS8PPAbwP/Gngf8AHgO8AvV9XBwIeAP5piHb8HfLWqXgccA/xpkh1HULtmgar69/RuRD0G2JHpf0sH0vt9HgZ8FHi2/S7/ETi99flSVb2uql4L3A0snmKTS4F3V9Wh9H7nfz6cbzY7efho9ru/qu4ASHIXcF1VVZI7gIXAzsCyJPvTe6zIdlOs4/XAiZOO2e4AvIre/5TSxpjutwRwfVU9BTyV5Angy639Dnp/1AAcmOQjwFzgFfTua+okeQXwr4AvJt2Tc142jC8yWxkKs99zk8ZfmDT9Ar3//h+m9z/jbyZZCNwwxToC/FZV+fBBba4pf0tJDmfm3yrAZ4GTq+q2JG8Hjl5v/dsAj1fVQS9u2VsPDx9pZ+D/tvG3T9PnGuDdaX96JTl4BHVpdtrc39JOwENJtgNOW39mVT0J3J/k1Lb+JHntZta8VTEU9N+AP07y9/QeLzKVD9M7rHR7kjvbtLQpNve39EHgZuBaeufDpnIasDjJbcBd+F6WjeJjLiRJHfcUJEkdQ0GS1DEUJEkdQ0GS1DEUJEkdQ0HaRO05PncluT3Jre0GLOklzTuapU2Q5Ejg14FDquq5JLsD24+5LGmzuacgbZr5wKNV9RxAVT1aVWuSHJrkxvaEzmuSzE8ypz3Z82iAJH+c5KPjLF6ajjevSZugPXjtG8DLgb8DLgX+AbgROKmq1ib5d8CvVdWZSX4RuBw4m95d5IdX1Y/GU700PQ8fSZugqp5Ocijwb+g9AvpS4CP0Hv98bXu0z7bAQ63/XUk+T+/Jn0caCNpSGQrSJqqqH9N7quwN7VHkZwF3VdWR0yzyGuBxYM/RVChtPM8pSJsgyQHtHRTrHETv/RLz2klokmzXDhuR5BRgN+CXgfOTzB11zdIgPKcgbYJ26Oh/0HvZy/PAKmAJsAA4n94jyecAfwZcSe98w3FV9WCSs4FDq2rROGqX+jEUJEkdDx9JkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjr/H9ALt9/30eEgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Sex.value_counts().plot(kind=\"bar\", color=[\"LightBlue\", \"Pink\"],rot=0)\n",
    "plt.xlabel(\"Sex\")\n",
    "plt.ylabel(\"No: Of Passengers\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17c84fd19e8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,8))\n",
    "plt.scatter(df.Age[df.Survived==0], df.SibSp[df.Survived==0], label=\"didnt Survive\")\n",
    "plt.scatter(df.Age[df.Survived==1], df.SibSp[df.Survived==1], label=\"Survived\", color=\"salmon\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Sibling\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"].fillna(df[\"Age\"].mean(),inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Fare           0\n",
       "Embarked       2\n",
       "Title          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    646\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Embarked'].replace(\"S\",1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['PassengerId'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-98-77d92e811ab8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"PassengerId\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3995\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3996\u001b[0m             \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3997\u001b[1;33m             \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3998\u001b[0m         )\n\u001b[0;32m   3999\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3934\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3935\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3936\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3938\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3968\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3969\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3970\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3971\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5015\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5016\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5017\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5018\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5019\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['PassengerId'] not found in axis\""
     ]
    }
   ],
   "source": [
    "df = df.drop(\"PassengerId\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</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>3.0</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</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>1.0</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  Sex  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris    0   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1   \n",
       "2         1       3                             Heikkinen, Miss. Laina    1   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1   \n",
       "4         0       3                           Allen, Mr. William Henry    0   \n",
       "\n",
       "    Age  SibSp  Parch     Fare  Embarked Title  \n",
       "0  22.0      1      0   7.2500       1.0    Mr  \n",
       "1  38.0      1      0  71.2833       3.0   Mrs  \n",
       "2  26.0      0      0   7.9250       1.0  Miss  \n",
       "3  35.0      1      0  53.1000       1.0   Mrs  \n",
       "4  35.0      0      0   8.0500       1.0    Mr  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex\"].replace(\"female\",1, inplace=True)\n",
    "df[\"Sex\"].replace(\"male\",0, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17c85341ac8>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "sns.heatmap(df.corr(), cmap=\"YlGnBu\", annot=True,linewidth=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy()\n",
    "df1.drop(\"Name\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2462f215c50>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "sns.heatmap(df1.corr(), cmap=\"YlGnBu\",annot=True,linewidth=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title_Col</th>\n",
       "      <th>Title_Dr</th>\n",
       "      <th>Title_Mas</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Rev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\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>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Title_Col  \\\n",
       "0         0       3    0  22.0      1      0   7.2500       1.0          0   \n",
       "1         1       1    1  38.0      1      0  71.2833       3.0          0   \n",
       "2         1       3    1  26.0      0      0   7.9250       1.0          0   \n",
       "3         1       1    1  35.0      1      0  53.1000       1.0          0   \n",
       "4         0       3    0  35.0      0      0   8.0500       1.0          0   \n",
       "\n",
       "   Title_Dr  Title_Mas  Title_Miss  Title_Mr  Title_Mrs  Title_Rev  \n",
       "0         0          0           0         1          0          0  \n",
       "1         0          0           0         0          1          0  \n",
       "2         0          0           1         0          0          0  \n",
       "3         0          0           0         0          1          0  \n",
       "4         0          0           0         1          0          0  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.get_dummies(df1)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting Data into test and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into train and test\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Title_Col  Title_Dr  \\\n",
       " 0       3    0  22.0      1      0   7.2500       1.0          0         0   \n",
       " 1       1    1  38.0      1      0  71.2833       3.0          0         0   \n",
       " 2       3    1  26.0      0      0   7.9250       1.0          0         0   \n",
       " 3       1    1  35.0      1      0  53.1000       1.0          0         0   \n",
       " 4       3    0  35.0      0      0   8.0500       1.0          0         0   \n",
       " \n",
       "    Title_Mas  Title_Miss  Title_Mr  Title_Mrs  Title_Rev  \n",
       " 0          0           0         1          0          0  \n",
       " 1          0           0         0          1          0  \n",
       " 2          0           1         0          0          0  \n",
       " 3          0           0         0          1          0  \n",
       " 4          0           0         1          0          0  ,\n",
       " 0    0\n",
       " 1    1\n",
       " 2    1\n",
       " 3    1\n",
       " 4    0\n",
       " Name: Survived, dtype: int64)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= df1.drop(\"Survived\", axis=1)\n",
    "y = df1[\"Survived\"]\n",
    "x.head(),y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8356741573033708\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8268156424581006"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = LogisticRegression(max_iter=500)\n",
    "model1.fit(X_train, Y_train)\n",
    "print(model.score(X_train, Y_train))\n",
    "model1.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.85004628,  1.31458474, -0.02292382, -0.4706533 , -0.35503591,\n",
       "         0.00432618,  0.23914959, -0.30463227, -0.40530328,  1.59002356,\n",
       "         0.10041032, -1.29093085,  1.21417443, -0.91157531]])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7963483146067416\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7206703910614525"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = KNeighborsClassifier()\n",
    "model2.fit(X_train, Y_train)\n",
    "print(model2.score(X_train,Y_train))\n",
    "model2.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9831460674157303\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8212290502793296"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model3 = RandomForestClassifier()\n",
    "model3.fit(X_train, Y_train)\n",
    "print(model3.score(X_train, Y_train))\n",
    "model3.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8212290502793296"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model4 = LogisticRegression(solver=\"liblinear\",C=3,max_iter=500)\n",
    "model4.fit(X_train, Y_train)\n",
    "model4.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"test.csv\")\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Col         2\n",
       "Rev         2\n",
       "Ms          1\n",
       "Dona        1\n",
       "Dr          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Title\"] = data[\"Name\"].str.extract(r' ([A-Za-z]+)\\.')\n",
    "data.Title.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({\"Dona\":\"Mrs\",\"Ms\":\"Mrs\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        74\n",
       "Master     21\n",
       "Col         2\n",
       "Rev         2\n",
       "Dr          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"Cabin\",\"Ticket\"], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>C</td>\n",
       "      <td>Master</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Fare Embarked   Title  \n",
       "0      male  34.5      0      0    7.8292        Q      Mr  \n",
       "1    female  47.0      1      0    7.0000        S     Mrs  \n",
       "2      male  62.0      0      0    9.6875        Q      Mr  \n",
       "3      male  27.0      0      0    8.6625        S      Mr  \n",
       "4    female  22.0      1      1   12.2875        S     Mrs  \n",
       "..      ...   ...    ...    ...       ...      ...     ...  \n",
       "413    male   NaN      0      0    8.0500        S      Mr  \n",
       "414  female  39.0      0      0  108.9000        C     Mrs  \n",
       "415    male  38.5      0      0    7.2500        S      Mr  \n",
       "416    male   NaN      0      0    8.0500        S      Mr  \n",
       "417    male   NaN      1      1   22.3583        C  Master  \n",
       "\n",
       "[418 rows x 10 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Embarked\"] = data[\"Embarked\"].replace({\"S\":1,\"Q\":2,\"C\":3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"Age\"].fillna(df[\"Age\"].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Title_Col  Title_Dr  \\\n",
       "0       3    0  34.5      0      0   7.8292         2          0         0   \n",
       "1       3    1  47.0      1      0   7.0000         1          0         0   \n",
       "2       2    0  62.0      0      0   9.6875         2          0         0   \n",
       "3       3    0  27.0      0      0   8.6625         1          0         0   \n",
       "4       3    1  22.0      1      1  12.2875         1          0         0   \n",
       "\n",
       "   Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Rev  \n",
       "0             0           0         1          0          0  \n",
       "1             0           0         0          1          0  \n",
       "2             0           0         1          0          0  \n",
       "3             0           0         1          0          0  \n",
       "4             0           0         0          1          0  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(\"PassengerId\",axis=1,inplace=True)\n",
    "data = pd.get_dummies(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>413</th>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1306</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.653446</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
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       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.653446</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass  Sex        Age  SibSp  Parch      Fare  embark\n",
       "0            892       3    1  34.500000      0      0    7.8292       1\n",
       "1            893       3    0  47.000000      1      0    7.0000       2\n",
       "2            894       2    1  62.000000      0      0    9.6875       1\n",
       "3            895       3    1  27.000000      0      0    8.6625       2\n",
       "4            896       3    0  22.000000      1      1   12.2875       2\n",
       "..           ...     ...  ...        ...    ...    ...       ...     ...\n",
       "413         1305       3    1  29.653446      0      0    8.0500       2\n",
       "414         1306       1    0  39.000000      0      0  108.9000       0\n",
       "415         1307       3    1  38.500000      0      0    7.2500       2\n",
       "416         1308       3    1  29.653446      0      0    8.0500       2\n",
       "417         1309       3    1  29.653446      1      1   22.3583       0\n",
       "\n",
       "[418 rows x 8 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "data[\"embark\"] = encoder.fit_transform(data[\"Embarked\"])\n",
    "data.drop(\"Embarked\",axis=1,inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 14)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Fare\"].fillna(df1[\"Fare\"].median(),inplace=True)\n",
    "data.isna().sum()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>22.3583</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>418 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age  SibSp  Parch      Fare  Embarked  Title_Col  \\\n",
       "0         3    0  34.500000      0      0    7.8292         2          0   \n",
       "1         3    1  47.000000      1      0    7.0000         1          0   \n",
       "2         2    0  62.000000      0      0    9.6875         2          0   \n",
       "3         3    0  27.000000      0      0    8.6625         1          0   \n",
       "4         3    1  22.000000      1      1   12.2875         1          0   \n",
       "..      ...  ...        ...    ...    ...       ...       ...        ...   \n",
       "413       3    0  29.699118      0      0    8.0500         1          0   \n",
       "414       1    1  39.000000      0      0  108.9000         3          0   \n",
       "415       3    0  38.500000      0      0    7.2500         1          0   \n",
       "416       3    0  29.699118      0      0    8.0500         1          0   \n",
       "417       3    0  29.699118      1      1   22.3583         3          0   \n",
       "\n",
       "     Title_Dr  Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Rev  \n",
       "0           0             0           0         1          0          0  \n",
       "1           0             0           0         0          1          0  \n",
       "2           0             0           0         1          0          0  \n",
       "3           0             0           0         1          0          0  \n",
       "4           0             0           0         0          1          0  \n",
       "..        ...           ...         ...       ...        ...        ...  \n",
       "413         0             0           0         1          0          0  \n",
       "414         0             0           0         0          1          0  \n",
       "415         0             0           0         1          0          0  \n",
       "416         0             0           0         1          0          0  \n",
       "417         0             1           0         0          0          0  \n",
       "\n",
       "[418 rows x 14 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = model2.predict(data)\n",
    "data_random[\"Survived\"]= arr\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"predict_KNN.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch   Fare  embark\n",
       "68           69         1       3    0  17.0      4      2  7.925       2"
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   "source": [
    "df.loc[df[\"PassengerId\"]==69]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(\"Survived\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_random = data.copy()"
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       "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Title_Col  Title_Dr  \\\n",
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    "data_random.head()"
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  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
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       "     Pclass  Sex        Age  SibSp  Parch      Fare  Embarked  Title_Col  \\\n",
       "0         3    0  34.500000      0      0    7.8292         2          0   \n",
       "1         3    1  47.000000      1      0    7.0000         1          0   \n",
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       "..      ...  ...        ...    ...    ...       ...       ...        ...   \n",
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       "\n",
       "     Title_Dr  Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Rev  \n",
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       "..        ...           ...         ...       ...        ...        ...  \n",
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       "\n",
       "[418 rows x 14 columns]"
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     },
     "execution_count": 168,
     "metadata": {},
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title_Col</th>\n",
       "      <th>Title_Dr</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Rev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age  SibSp  Parch      Fare  Embarked  Title_Col  \\\n",
       "0         3    0  34.500000      0      0    7.8292         2          0   \n",
       "1         3    1  47.000000      1      0    7.0000         1          0   \n",
       "2         2    0  62.000000      0      0    9.6875         2          0   \n",
       "3         3    0  27.000000      0      0    8.6625         1          0   \n",
       "4         3    1  22.000000      1      1   12.2875         1          0   \n",
       "..      ...  ...        ...    ...    ...       ...       ...        ...   \n",
       "413       3    0  29.699118      0      0    8.0500         1          0   \n",
       "414       1    1  39.000000      0      0  108.9000         3          0   \n",
       "415       3    0  38.500000      0      0    7.2500         1          0   \n",
       "416       3    0  29.699118      0      0    8.0500         1          0   \n",
       "417       3    0  29.699118      1      1   22.3583         3          0   \n",
       "\n",
       "     Title_Dr  Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Rev  \n",
       "0           0             0           0         1          0          0  \n",
       "1           0             0           0         0          1          0  \n",
       "2           0             0           0         1          0          0  \n",
       "3           0             0           0         1          0          0  \n",
       "4           0             0           0         0          1          0  \n",
       "..        ...           ...         ...       ...        ...        ...  \n",
       "413         0             0           0         1          0          0  \n",
       "414         0             0           0         0          1          0  \n",
       "415         0             0           0         1          0          0  \n",
       "416         0             0           0         1          0          0  \n",
       "417         0             1           0         0          0          0  \n",
       "\n",
       "[418 rows x 14 columns]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ran = data.copy()\n",
    "data_ran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = model2.predict(data_ran)\n",
    "data_ran[\"Survived\"]= arr\n",
    "data_ran.to_csv(\"predict_ran.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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