titanic-without-using-Pipelines.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d012ef54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8d4518e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>337</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mr. Thomas Clinton</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.6000</td>\n",
       "      <td>C2</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Drazenoic, Mr. Jozef</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
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       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>187</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Brien, Mrs. Thomas (Johanna \"Hannah\" Godfrey)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370365</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "336          337         0       1   \n",
       "130          131         0       3   \n",
       "873          874         0       3   \n",
       "186          187         1       3   \n",
       "6              7         0       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "336                        Pears, Mr. Thomas Clinton    male  29.0      1   \n",
       "130                             Drazenoic, Mr. Jozef    male  33.0      0   \n",
       "873                      Vander Cruyssen, Mr. Victor    male  47.0      0   \n",
       "186  O'Brien, Mrs. Thomas (Johanna \"Hannah\" Godfrey)  female   NaN      1   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "\n",
       "     Parch  Ticket     Fare Cabin Embarked  \n",
       "336      0  113776  66.6000    C2        S  \n",
       "130      0  349241   7.8958   NaN        C  \n",
       "873      0  345765   9.0000   NaN        S  \n",
       "186      0  370365  15.5000   NaN        Q  \n",
       "6        0   17463  51.8625   E46        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('(Titanic)train.csv')\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65fe518e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['PassengerId','Name','Ticket','Cabin'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "617571a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>Embarked</th>\n",
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       "      <th>102</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>77.2875</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>28.0</td>\n",
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       "      <td>57.0</td>\n",
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       "      <td>12.3500</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "102         0       1    male  21.0      0      1  77.2875        S\n",
       "291         1       1  female  19.0      1      0  91.0792        C\n",
       "355         0       3    male  28.0      0      0   9.5000        S\n",
       "626         0       2    male  57.0      0      0  12.3500        Q\n",
       "412         1       1  female  33.0      1      0  90.0000        Q"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0becc4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test= train_test_split(df.drop(columns=['Survived']),df['Survived'],test_size=0.2,random_state=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f9395ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>357</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <th>326</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>61.0</td>\n",
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       "      <td>S</td>\n",
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       "      <td>1</td>\n",
       "      <td>female</td>\n",
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       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>S</td>\n",
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       "      <th>350</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>S</td>\n",
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      ],
      "text/plain": [
       "     Pclass     Sex   Age  SibSp  Parch      Fare Embarked\n",
       "357       2  female  38.0      0      0   13.0000        S\n",
       "326       3    male  61.0      0      0    6.2375        S\n",
       "31        1  female   NaN      1      0  146.5208        C\n",
       "318       1  female  31.0      0      2  164.8667        S\n",
       "350       3    male  23.0      0      0    9.2250        S"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97ec5b15",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "415    0\n",
       "829    1\n",
       "851    0\n",
       "97     1\n",
       "111    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa271401",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    object \n",
      " 3   Age       714 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Fare      891 non-null    float64\n",
      " 7   Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 55.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0cbfd33",
   "metadata": {},
   "source": [
    "### Filling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d2ad756",
   "metadata": {
    "scrolled": true
   },
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b90bfe06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying imputation\n",
    "si_age = SimpleImputer()# if the empty in brackets it means mean\n",
    "si_Embarked = SimpleImputer(strategy ='most_frequent')\n",
    "\n",
    "x_train_age = si_age.fit_transform(x_train[['Age']])\n",
    "x_train_embarked =si_Embarked.fit_transform(x_train[['Embarked']])\n",
    "\n",
    "x_test_age = si_age.transform(x_test[['Age']])\n",
    "x_test_embarked =si_Embarked.transform(x_test[['Embarked']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5bf8df04",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_embarked"
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  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2eacae56",
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       "       [44.        ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5911e6fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# oneHot encoding for sex & embarked\n",
    "ohe_sex = OneHotEncoder(sparse=False,handle_unknown ='ignore')\n",
    "ohe_embarked = OneHotEncoder(sparse=False,handle_unknown ='ignore')\n",
    "\n",
    "x_train_sex = ohe_sex.fit_transform(x_train[['Sex']])\n",
    "x_train_embarked =ohe_embarked.fit_transform(x_train_embarked)\n",
    "\n",
    "x_test_sex = ohe_sex.transform(x_test[['Sex']])\n",
    "x_test_embarked = ohe_embarked.transform(x_test_embarked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41c56281",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 1.],\n",
       "       ...,\n",
       "       [1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4cf9ef51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       ...,\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "13c8bb76",
   "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>853</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "853       1  female  16.0      0      1  39.4000        S\n",
       "39        3  female  14.0      1      0  11.2417        C\n",
       "294       3    male  24.0      0      0   7.8958        S\n",
       "295       1    male   NaN      0      0  27.7208        C\n",
       "725       3    male  20.0      0      0   8.6625        S"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0a612942",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rem = x_train.drop(columns=['Sex','Age','Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b3117362",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_rem = x_test.drop(columns=['Sex','Age','Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d1f632d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_transformed = np.concatenate((x_train_rem,x_train_age,x_train_sex,x_train_embarked),axis=1)\n",
    "x_test_transformed = np.concatenate((x_test_rem,x_test_age,x_test_sex,x_test_embarked),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a7404287",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(712, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1669cd08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179, 10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "02a275cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(x_train_transformed,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2f88b526",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred =clf.predict(x_test_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2cdcca1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9d4b59b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7653631284916201"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b39b47f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c442ecfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(ohe_sex,open('models/ohe_sex.pkl','wb'))\n",
    "pickle.dump(ohe_embarked,open('models/ohe_embarked.pkl','wb'))\n",
    "pickle.dump(clf,open('models/clf.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "047a0a41",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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