kaggle_titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <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",
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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 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  "
      ]
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     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import linear_model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "pd.options.mode.chained_assignment = None\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "train = r\"E:\\kaggle\\titanic competition\\train.csv\"\n",
    "test = r\"E:\\kaggle\\titanic competition\\test.csv\"\n",
    "df_train = pd.read_csv(train,delimiter=',')\n",
    "df_test  = pd.read_csv(test,delimiter=',')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.drop(['Cabin','Ticket','Name','PassengerId'],axis=1,inplace=True)\n",
    "df_test.drop(['Cabin','Ticket','Name','PassengerId'],axis=1,inplace=True)\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
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       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
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       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>12.2875</td>\n",
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      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0       3    male  34.5      0      0   7.8292        Q\n",
       "1       3  female  47.0      1      0   7.0000        S\n",
       "2       2    male  62.0      0      0   9.6875        Q\n",
       "3       3    male  27.0      0      0   8.6625        S\n",
       "4       3  female  22.0      1      1  12.2875        S"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       0\n",
       "Sex          0\n",
       "Age         86\n",
       "SibSp        0\n",
       "Parch        0\n",
       "Fare         1\n",
       "Embarked     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['Age'].fillna(df_train['Age'].median(),inplace=True)\n",
    "df_test['Age'].fillna(df_test['Age'].median(),inplace=True)\n",
    "df_test['Fare'].fillna(df_test['Age'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    2\n",
       "dtype: int64"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_train.isnull().sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <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>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": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0       3    male  34.5      0      0   7.8292        Q\n",
       "1       3  female  47.0      1      0   7.0000        S\n",
       "2       2    male  62.0      0      0   9.6875        Q\n",
       "3       3    male  27.0      0      0   8.6625        S\n",
       "4       3  female  22.0      1      1  12.2875        S"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked\n",
       "0         0       2    1   28      1      0    18         2\n",
       "1         1       0    0   51      1      0   207         0\n",
       "2         1       2    0   34      0      0    41         2\n",
       "3         1       0    0   47      1      0   189         2\n",
       "4         0       2    1   47      0      0    43         2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le       = preprocessing.LabelEncoder()\n",
    "df_train = df_train.apply(le.fit_transform)\n",
    "df_test  = df_test.apply(le.fit_transform)\n",
    "df_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Parch  Fare  Embarked\n",
       "0       2    1   44      0      0    24         1\n",
       "1       2    0   60      1      0     5         2\n",
       "2       1    1   74      0      0    41         1\n",
       "3       2    1   34      0      0    34         2\n",
       "4       2    0   27      1      1    46         2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(8,6))\n",
    "sns.heatmap(df_train.corr(),vmin=-0.8, annot=True, cmap='coolwarm',ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ydata = df_train['Survived'] \n",
    "Xdata = df_train[['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']]\n",
    "Xdata = (Xdata - Xdata.mean())/Xdata.std()\n",
    "\n",
    "x_train, x_test, y_train, y_test =train_test_split(Xdata,Ydata,train_size=0.8,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 0 1 1 0 0 1 0 1 0 1 0 1 0 1 1 1 0 0 0 0 1 1 1 0 0 1 1 1 1 1 1 1 0\n",
      " 0 1 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 0\n",
      " 1 1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 1 0 0 1 0 1 1 1 1 0 1 0 0 0 0 1 0 0 0 0\n",
      " 0 0 1 1 1 0 1 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 1\n",
      " 1 0 0 0 1 0 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(x_train, y_train)\n",
    "\n",
    "Y_pred = logreg.predict(x_test)\n",
    "print(Y_pred)\n",
    "\n",
    "acc_log = round(logreg.score(x_train, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "sgd = linear_model.SGDClassifier(max_iter=5, tol=None)\n",
    "sgd.fit(x_train, y_train)\n",
    "Y_pred = sgd.predict(x_test)\n",
    "\n",
    "sgd.score(x_train, y_train)\n",
    "\n",
    "acc_sgd = round(sgd.score(x_train, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98.59\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(x_train, y_train)\n",
    "\n",
    "Y_prediction = random_forest.predict(x_test)\n",
    "\n",
    "random_forest.score(x_train, y_train)\n",
    "acc_random_forest = round(random_forest.score(x_train,y_train) * 100, 2)\n",
    "print(acc_random_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 0 0 1 1 1 1 0\n",
      " 0 1 0 1 0 0 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0\n",
      " 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 1 1 0 1 0 0 0 0 1 0 1 0 0\n",
      " 0 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1\n",
      " 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3) \n",
    "knn.fit(x_train, y_train)  \n",
    "Y_pred = knn.predict(x_test) \n",
    "print(Y_pred)\n",
    "acc_knn = round(knn.score(x_train, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian = GaussianNB() \n",
    "gaussian.fit(x_train, y_train)  \n",
    "Y_pred = gaussian.predict(x_test)  \n",
    "acc_gaussian = round(gaussian.score(x_train,y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in Perceptron in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "perceptron = Perceptron(max_iter=5)\n",
    "perceptron.fit(x_train, y_train)\n",
    "\n",
    "Y_pred = perceptron.predict(x_test)\n",
    "\n",
    "acc_perceptron = round(perceptron.score(x_train, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(x_train, y_train)\n",
    "\n",
    "Y_pred = linear_svc.predict(x_test)\n",
    "\n",
    "acc_linear_svc = round(linear_svc.score(x_train, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_tree = DecisionTreeClassifier() \n",
    "decision_tree.fit(x_train, y_train)  \n",
    "Y_pred = decision_tree.predict(x_test) \n",
    "acc_decision_tree = round(decision_tree.score(x_train, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98.59</th>\n",
       "      <td>Random Forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98.59</th>\n",
       "      <td>Decision Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87.20</th>\n",
       "      <td>KNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78.90</th>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78.76</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.53</th>\n",
       "      <td>Naive Bayes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74.82</th>\n",
       "      <td>Perceptron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69.20</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model\n",
       "Score                            \n",
       "98.59               Random Forest\n",
       "98.59               Decision Tree\n",
       "87.20                         KNN\n",
       "78.90         Logistic Regression\n",
       "78.76     Support Vector Machines\n",
       "75.53                 Naive Bayes\n",
       "74.82                  Perceptron\n",
       "69.20  Stochastic Gradient Decent"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_linear_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_decision_tree]})\n",
    "result_df = results.sort_values(by='Score', ascending=False)\n",
    "result_df = result_df.set_index('Score')\n",
    "result_df.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [0.81944444 0.83333333 0.76388889 0.81944444 0.8028169  0.8028169\n",
      " 0.81690141 0.75714286 0.77142857 0.77142857]\n",
      "Mean: 0.7958646322378716\n",
      "Standard Deviation: 0.026021527358756708\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100)\n",
    "scores = cross_val_score(rf, x_train, y_train, cv=10, scoring = \"accuracy\")\n",
    "print(\"Scores:\", scores)\n",
    "print(\"Mean:\", scores.mean())\n",
    "print(\"Standard Deviation:\", scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          importance\n",
       "feature             \n",
       "Fare           0.289\n",
       "Age            0.278\n",
       "Sex            0.223\n",
       "Pclass         0.081\n",
       "SibSp          0.052\n",
       "Parch          0.041\n",
       "Embarked       0.036"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = pd.DataFrame({'feature':x_train.columns,'importance':np.round(random_forest.feature_importances_,3)})\n",
    "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
    "importances.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24fcee13f60>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', 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, n_estimators=100, n_jobs=-1,\n",
       "            oob_score=True, random_state=1, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'criterion': ['gini', 'entropy'], 'min_samples_leaf': [1, 5, 10, 25, 50, 70], 'min_samples_split': [2, 4, 10, 12, 16, 18, 25, 35], 'n_estimators': [100, 400, 700, 1000, 1500]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = { \"criterion\" : [\"gini\", \"entropy\"], \"min_samples_leaf\" : [1, 5, 10, 25, 50, 70], \"min_samples_split\" : [2, 4, 10, 12, 16, 18, 25, 35], \"n_estimators\": [100, 400, 700, 1000, 1500]}\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "rf = RandomForestClassifier(n_estimators=100, max_features='auto', oob_score=True, random_state=1, n_jobs=-1)\n",
    "clf = GridSearchCV(estimator=rf, param_grid=param_grid, n_jobs=-1)\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 10,\n",
       " 'n_estimators': 1500}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc_random_forest : 90.44\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(criterion = 'entropy', \n",
    "                                       min_samples_leaf = 1, \n",
    "                                       min_samples_split = 10,   \n",
    "                                       n_estimators=1500, \n",
    "                                       max_features='auto', \n",
    "                                       oob_score=True, \n",
    "                                       random_state=1, \n",
    "                                       n_jobs=-1)\n",
    "\n",
    "random_forest.fit(x_train, y_train)\n",
    "Y_prediction_ = random_forest.predict(x_test)\n",
    "random_forest.score(x_train, y_train)\n",
    "\n",
    "#print(\"oob score:\", round(random_forest.oob_score_, 4)*100, \"%\")\n",
    "acc_random_forest = round(random_forest.score(x_train,y_train) * 100, 2)\n",
    "print('acc_random_forest :',acc_random_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 1 1 0 0 1 0 0 1 1 1 1 0\n",
      " 0 1 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 0\n",
      " 1 1 0 0 1 1 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 0 1 0 0\n",
      " 0 0 1 1 1 0 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1\n",
      " 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(Y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC-Score: 0.9734158315618989\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "# getting the probabilities of our predictions\n",
    "y_scores = random_forest.predict_proba(x_train)\n",
    "y_scores = y_scores[:,1]\n",
    "from sklearn.metrics import roc_auc_score\n",
    "r_a_score = roc_auc_score(y_train, y_scores)\n",
    "print(\"ROC-AUC-Score:\", r_a_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtest = df_test[['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked']]\n",
    "Xtest = (Xtest - Xtest.mean())/Xtest.std()\n",
    "\n",
    "Y_prediction_ = random_forest.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_prediction_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>0</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         1\n",
       "4          896         0\n",
       "5          897         0\n",
       "6          898         0\n",
       "7          899         0\n",
       "8          900         1\n",
       "9          901         0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_submission=pd.read_csv(test,delimiter=',')\n",
    "df_submission.drop(df_submission.columns.difference(['PassengerId']), 1, inplace=True)\n",
    "df_submission['Survived']=Y_prediction_\n",
    "df_submission.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_submission.to_csv(r'E:\\kaggle\\titanic competition\\gender_submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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