Titanic-Survival-Prediction-using-KNN and Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survival Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing all Library's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('titanic_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data=pd.read_csv('test.csv')\n",
    "testing_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff=data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \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",
       "\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  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Non required columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['Ticket','Cabin','Name'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data.drop(['Ticket','Cabin','Name'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</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>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0          892       3    male  34.5      0      0   7.8292        Q\n",
       "1          893       3  female  47.0      1      0   7.0000        S\n",
       "2          894       2    male  62.0      0      0   9.6875        Q\n",
       "3          895       3    male  27.0      0      0   8.6625        S\n",
       "4          896       3  female  22.0      1      1  12.2875        S"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting countplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f538b3ac8>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {\n",
    "    'weight' : 'bold',\n",
    "    'size'   : 22\n",
    "}\n",
    "plt.figure(figsize=(12,9))\n",
    "plt.title(\"Survival On Basis of Number of sibling\", fontdict=font)\n",
    "plt.legend(loc='best')\n",
    "sns.countplot(x='SibSp',hue='Survived',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f537c5128>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.title(\"Survival On Basis of Number of parent\", fontdict=font)\n",
    "plt.legend(loc='best')\n",
    "sns.countplot(x='Parch',hue='Survived',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0            1         0       3    male  22.0      1      0   7.2500        S\n",
       "1            2         1       1  female  38.0      1      0  71.2833        C"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Sex</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>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch    Fare Embarked\n",
       "0          892       3    male  34.5      0      0  7.8292        Q\n",
       "1          893       3  female  47.0      1      0  7.0000        S"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Age=data.Age.fillna(data.Age.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    False\n",
       "Survived       False\n",
       "Pclass         False\n",
       "Sex            False\n",
       "Age            False\n",
       "SibSp          False\n",
       "Parch          False\n",
       "Fare           False\n",
       "Embarked        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data.Age=testing_data.Age.fillna(data.Age.mean())\n",
    "testing_data.Fare=testing_data.Fare.fillna(data.Fare.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    False\n",
       "Pclass         False\n",
       "Sex            False\n",
       "Age            False\n",
       "SibSp          False\n",
       "Parch          False\n",
       "Fare           False\n",
       "Embarked       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0            1         0       3    male  22.0      1      0   7.2500        S\n",
       "1            2         1       1  female  38.0      1      0  71.2833        C"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>PassengerId</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",
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       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch    Fare Embarked\n",
       "0          892       3    male  34.5      0      0  7.8292        Q\n",
       "1          893       3  female  47.0      1      0  7.0000        S"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating dummies( Done to convert categorical to continous )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   C  Q  S\n",
       "0  0  1  0\n",
       "1  0  0  1\n",
       "2  0  1  0\n",
       "3  0  0  1\n",
       "4  0  0  1"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark_d=pd.get_dummies(data.Embarked)\n",
    "embark_t=pd.get_dummies(testing_data.Embarked)\n",
    "sex_d=pd.get_dummies(data.Sex)\n",
    "sex_t=pd.get_dummies(testing_data.Sex)\n",
    "embark_t.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  \\\n",
       "0            1         0       3    male  22.0      1      0   7.2500   \n",
       "1            2         1       1  female  38.0      1      0  71.2833   \n",
       "2            3         1       3  female  26.0      0      0   7.9250   \n",
       "3            4         1       1  female  35.0      1      0  53.1000   \n",
       "4            5         0       3    male  35.0      0      0   8.0500   \n",
       "\n",
       "  Embarked  C  Q  S  female  male  \n",
       "0        S  0  0  1       0     1  \n",
       "1        C  1  0  0       1     0  \n",
       "2        S  0  0  1       1     0  \n",
       "3        S  0  0  1       1     0  \n",
       "4        S  0  0  1       0     1  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.concat([data,embark_d,sex_d],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Pclass</th>\n",
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       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
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       "      <td>S</td>\n",
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       "      <td>894</td>\n",
       "      <td>2</td>\n",
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       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch     Fare Embarked  C  Q  S  \\\n",
       "0          892       3    male  34.5      0      0   7.8292        Q  0  1  0   \n",
       "1          893       3  female  47.0      1      0   7.0000        S  0  0  1   \n",
       "2          894       2    male  62.0      0      0   9.6875        Q  0  1  0   \n",
       "3          895       3    male  27.0      0      0   8.6625        S  0  0  1   \n",
       "4          896       3  female  22.0      1      1  12.2875        S  0  0  1   \n",
       "\n",
       "   female  male  \n",
       "0       0     1  \n",
       "1       1     0  \n",
       "2       0     1  \n",
       "3       0     1  \n",
       "4       1     0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data=pd.concat([testing_data,embark_t,sex_t],axis=1)\n",
    "testing_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['Embarked','Sex'],axis=1,inplace=True)\n",
    "testing_data.drop(['Embarked','Sex'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  C  Q  S  \\\n",
       "0            1         0       3  22.0      1      0   7.2500  0  0  1   \n",
       "1            2         1       1  38.0      1      0  71.2833  1  0  0   \n",
       "\n",
       "   female  male  \n",
       "0       0     1  \n",
       "1       1     0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch    Fare  C  Q  S  female  male\n",
       "0          892       3  34.5      0      0  7.8292  0  1  0       0     1\n",
       "1          893       3  47.0      1      0  7.0000  0  0  1       1     0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y=data.drop(['Survived'],axis=1),data['Survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Testing And Training Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating 2 Models one Of KNN and Second Of Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=KNeighborsClassifier(n_neighbors=6)\n",
    "model2=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)\n",
    "model2.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=model.predict(x_test)\n",
    "prediction2=model2.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "kconfusion=confusion_matrix(prediction,y_test)\n",
    "dconfusion=confusion_matrix(prediction2,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[97, 54],\n",
       "       [ 8, 20]], dtype=int64)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kconfusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[85, 21],\n",
       "       [20, 53]], dtype=int64)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dconfusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.36"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score=round(accuracy_score(prediction,y_test)*100,2)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.09"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score1=round(accuracy_score(prediction2,y_test)*100,2)\n",
    "score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub1=model2.predict(testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_file1 = pd.DataFrame({'PassengerId': testing_data['PassengerId'], 'Survived': sub1})\n",
    "submission_file1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_file1.to_csv('DecisionSubmission.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Another Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('titanic_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Family']=df['SibSp']+df['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['SibSp','Parch','Cabin'],axis=1,inplace=True)"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</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>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>1</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>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</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>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</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  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             Ticket     Fare Embarked  Family  \n",
       "0         A/5 21171   7.2500        S       1  \n",
       "1          PC 17599  71.2833        C       1  \n",
       "2  STON/O2. 3101282   7.9250        S       0  \n",
       "3            113803  53.1000        S       1  \n",
       "4            373450   8.0500        S       0  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Allen, Mr. William Henry</td>\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  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             Ticket     Fare Embarked  Family  C  Q  S  female  male  \n",
       "0         A/5 21171   7.2500        S       1  0  0  1       0     1  \n",
       "1          PC 17599  71.2833        C       1  1  0  0       1     0  \n",
       "2  STON/O2. 3101282   7.9250        S       0  0  0  1       1     0  \n",
       "3            113803  53.1000        S       1  0  0  1       1     0  \n",
       "4            373450   8.0500        S       0  0  0  1       0     1  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.concat([df,embark_d,sex_d],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\n",
       "                                                Name   Age     Ticket  \\\n",
       "0                            Braund, Mr. Owen Harris  22.0  A/5 21171   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0   PC 17599   \n",
       "\n",
       "      Fare  Family  C  Q  S  female  male  \n",
       "0   7.2500       1  0  0  1       0     1  \n",
       "1  71.2833       1  1  0  0       1     0  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.drop(['Embarked','Sex'],axis=1,inplace=True)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Age=df.Age.fillna(df.Age.mean())\n",
    "testing_data.Fare=testing_data.Fare.fillna(data.Fare.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\n",
       "                                                Name   Age     Ticket  \\\n",
       "0                            Braund, Mr. Owen Harris  22.0  A/5 21171   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0   PC 17599   \n",
       "\n",
       "      Fare  Family  C  Q  S  female  male  \n",
       "0   7.2500       1  0  0  1       0     1  \n",
       "1  71.2833       1  1  0  0       1     0  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Not Considering Name And Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   PassengerId  Survived  Pclass   Age     Fare  Family  C  Q  S  female  male\n",
       "0            1         0       3  22.0   7.2500       1  0  0  1       0     1\n",
       "1            2         1       1  38.0  71.2833       1  1  0  0       1     0"
      ]
     },
     "execution_count": 118,
     "metadata": {},
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   ],
   "source": [
    "df.drop(['Name','Ticket'],axis=1,inplace=True)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
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       "   PassengerId  Pclass   Age  SibSp  Parch    Fare  C  Q  S  female  male\n",
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       "1          893       3  47.0      1      0  7.0000  0  0  1       1     0"
      ]
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     "execution_count": 119,
     "metadata": {},
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   ],
   "source": [
    "testing_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch    Fare  C  Q  S  female  male  \\\n",
       "0          892       3  34.5      0      0  7.8292  0  1  0       0     1   \n",
       "1          893       3  47.0      1      0  7.0000  0  0  1       1     0   \n",
       "\n",
       "   Family  \n",
       "0       0  \n",
       "1       1  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data['Family']=testing_data['SibSp']+testing_data['Parch']\n",
    "testing_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>female</th>\n",
       "      <th>male</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>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age     Fare  Family  C  Q  S  female  male\n",
       "0            1         0       3  22.0   7.2500       1  0  0  1       0     1\n",
       "1            2         1       1  38.0  71.2833       1  1  0  0       1     0\n",
       "2            3         1       3  26.0   7.9250       0  0  0  1       1     0\n",
       "3            4         1       1  35.0  53.1000       1  0  0  1       1     0\n",
       "4            5         0       3  35.0   8.0500       0  0  0  1       0     1"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y=df.drop(['Survived'],axis=1),df['Survived']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=6, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model=KNeighborsClassifier(n_neighbors=6)\n",
    "\n",
    "model.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data.drop(['SibSp','Parch'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_prediction=model.predict(testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "submission_file1 = pd.DataFrame({'PassengerId': testing_data['PassengerId'], 'Survived': new_prediction})\n",
    "submission_file1.head()\n",
    "\n",
    "submission_file1.to_csv('KnnSubmission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.36"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score=round(accuracy_score(prediction,y_test)*100,2)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=data[(data.female==1)]\n",
    "accuracy.shape[0]"
   ]
  }
 ],
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