titanic_prediction_trainData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b639b7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65461ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3580ae0c",
   "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",
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       "    <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e6a844f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0ecf6fb",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#deal with missing data\n",
    "#train.isnull()\n",
    "train.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41338723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using graph method\n",
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "11977372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5835e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=train,hue='Sex',palette=\"rainbow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24fc038a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=train,hue='Pclass',palette=\"rainbow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70e3749b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train[\"Age\"].dropna(),kde=False,color='darkred' , bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d1fd9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "729d8d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"SibSp\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70288c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist(color='green',bins=40,figsize=(8,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b26386fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data cleaning\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a90e3c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#returning values are guessed by seeinng box plot average line in middle , parameter is 2 columns age and Pclass\n",
    "def ageFilling(cols):\n",
    "    Age=cols[0]\n",
    "    Pclass=cols[1]\n",
    "    if pd.isnull(Age):\n",
    "        if Pclass==1:\n",
    "            return 37\n",
    "        elif Pclass==2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 24\n",
    "    else:\n",
    "        return Age\n",
    "           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e559ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age']=train[['Age','Pclass']].apply(ageFilling,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "572ab819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b94f2de",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Embarked'].fillna('C',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1d0b0ad9",
   "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",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae7f2920",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping cabin column\n",
    "train.drop('Cabin',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e9b5871f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8dafcfea",
   "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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
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       "  </tbody>\n",
       "</table>\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            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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    "train.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d53f5d5d",
   "metadata": {},
   "outputs": [
    {
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       "   Q  S\n",
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     "execution_count": 23,
     "metadata": {},
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   ],
   "source": [
    "pd.get_dummies(train['Embarked'],drop_first=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3a2107f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting string booleans to integers\n",
    "embark =pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4ecaed29",
   "metadata": {},
   "outputs": [
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       "   male\n",
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    "pd.get_dummies(train['Sex'],drop_first=True).head()"
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  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "132e9e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex =pd.get_dummies(train['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ab0d3221",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
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   "cell_type": "code",
   "execution_count": 28,
   "id": "6e328e2d",
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   "outputs": [
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       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
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   "cell_type": "code",
   "execution_count": 29,
   "id": "43aec99b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca92c59c",
   "metadata": {},
   "outputs": [
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   "id": "5b1a9d73",
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   "outputs": [
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      "text/plain": [
       "     PassengerId  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0              1       3  22.0      1      0   7.2500     1  0  1\n",
       "1              2       1  38.0      1      0  71.2833     0  0  0\n",
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       "3              4       1  35.0      1      0  53.1000     0  0  1\n",
       "4              5       3  35.0      0      0   8.0500     1  0  1\n",
       "..           ...     ...   ...    ...    ...      ...   ... .. ..\n",
       "886          887       2  27.0      0      0  13.0000     1  0  1\n",
       "887          888       1  19.0      0      0  30.0000     0  0  1\n",
       "888          889       3  24.0      1      2  23.4500     0  0  1\n",
       "889          890       1  26.0      0      0  30.0000     1  0  0\n",
       "890          891       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('Survived',axis=1)\n",
    "#kyuki ye dependent h ,not imcplace , just to see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4688527c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "59a0e099",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(train.drop(['Survived','PassengerId'],axis=1),train['Survived'],test_size=0.30,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d756b57f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9e502630",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model = LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "34111875",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model.fit(X_train.values,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "908d4f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=Model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ee686678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for model deployment\n",
    "Model2 = LogisticRegression(max_iter=1000)\n",
    "Model2.fit(X_train.values,y_train.values)\n",
    "predictions2=Model2.predict(X_test.values)\n",
    "pickle.dump(Model2,open(\"ModelTrainedDeploy\",'wb'))\n",
    "predictions2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b0338fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "da9a2f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy =confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0a30db88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[134,  20],\n",
       "       [ 38,  76]], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "195ddbeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "680d478c",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy=accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "37e6ccc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7835820895522388"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b5ef995c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4e170940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>1</td>\n",
       "      <td>45.5</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>227.5250</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>32.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "<p>268 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass   Age  SibSp  Parch      Fare  male  Q  S\n",
       "331       1  45.5      0      0   28.5000     1  0  1\n",
       "700       1  18.0      1      0  227.5250     0  0  0\n",
       "748       1  19.0      1      0   53.1000     1  0  1\n",
       "751       3   6.0      0      1   12.4750     1  0  1\n",
       "481       2  29.0      0      0    0.0000     1  0  1\n",
       "..      ...   ...    ...    ...       ...   ... .. ..\n",
       "388       3  24.0      0      0    7.7292     1  1  0\n",
       "416       2  34.0      1      1   32.5000     0  0  1\n",
       "407       2   3.0      1      1   18.7500     1  0  1\n",
       "482       3  50.0      0      0    8.0500     1  0  1\n",
       "829       1  62.0      0      0   80.0000     0  0  0\n",
       "\n",
       "[268 rows x 8 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c682a1de",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.rename(columns = {'PassengerId':'PassengerId', 'Survived':'Survived'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "757f1977",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
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       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0              1         0       3  22.0      1      0   7.2500     1  0  1\n",
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       "..           ...       ...     ...   ...    ...    ...      ...   ... .. ..\n",
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       "890          891         0       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d0610b2d",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0              1         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1              2         1       1  38.0      1      0  71.2833     0  0  0\n",
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       "890          891         0       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0b6ec060",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Pclass','Age','SibSp','Parch','Fare','male','Q','S'],axis=1).to_csv(\"predicted.csv\",index=False,header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e50191e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "trainedModel=pickle.dump(Model,open(\"ModelTrained\",'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5b8e28b8",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0            1         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1            2         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2            3         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3            4         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4            5         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ab1b87b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66ab628d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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