Tackle_the_Titanic_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "TITANIC_PATH = os.path.join(\"datasets\", \"titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def load_titanic_data(filename, titanic_path=TITANIC_PATH):\n",
    "    csv_path = os.path.join(titanic_path, filename)\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = load_titanic_data(\"train.csv\")\n",
    "test_data = load_titanic_data(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "\n",
       "   Parch     Ticket     Fare Cabin Embarked  \n",
       "0      0  A/5 21171   7.2500   NaN        S  \n",
       "1      0   PC 17599  71.2833   C85        C  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Pclass\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Embarked\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
       "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
       "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
       "\n",
       "   Parch  Ticket    Fare Cabin Embarked  \n",
       "0      0  330911  7.8292   NaN        Q  \n",
       "1      0  363272  7.0000   NaN        S  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    (\"select_numeric\", DataFrameSelector([\"Age\", \"SibSp\", \"Parch\", \"Fare\"])),\n",
    "    (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.    ,  1.    ,  0.    ,  7.25  ],\n",
       "       [38.    ,  1.    ,  0.    , 71.2833],\n",
       "       [26.    ,  0.    ,  0.    ,  7.925 ],\n",
       "       ...,\n",
       "       [28.    ,  1.    ,  2.    , 23.45  ],\n",
       "       [26.    ,  0.    ,  0.    , 30.    ],\n",
       "       [32.    ,  0.    ,  0.    ,  7.75  ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline.fit_transform(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MostFrequentImputer(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        self.most_frequent_ = pd.Series([X[c].value_counts().index[0] for c in X],\n",
    "                                        index=X.columns)\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        return X.fillna(self.most_frequent_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline = Pipeline([\n",
    "    (\"select_cat\", DataFrameSelector([\"Pclass\", \"Sex\", \"Embarked\"])),\n",
    "    (\"imputer\", MostFrequentImputer()),\n",
    "    (\"cat_encoder\", OneHotEncoder(sparse=False)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 0., 0., 1.],\n",
       "       [1., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 0., 1., ..., 0., 0., 1.],\n",
       "       [1., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_pipeline.fit_transform(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "preprocess_pipeline = FeatureUnion(transformer_list=[\n",
    "    (\"num_pipeline\", num_pipeline),\n",
    "    (\"cat_pipeline\", cat_pipeline),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.,  1.,  0., ...,  0.,  0.,  1.],\n",
       "       [38.,  1.,  0., ...,  1.,  0.,  0.],\n",
       "       [26.,  0.,  0., ...,  0.,  0.,  1.],\n",
       "       ...,\n",
       "       [28.,  1.,  2., ...,  0.,  0.,  1.],\n",
       "       [26.,  0.,  0., ...,  1.,  0.,  0.],\n",
       "       [32.,  0.,  0., ...,  0.,  1.,  0.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = preprocess_pipeline.fit_transform(train_data)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train_data[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(gamma=\"auto\")\n",
    "svm_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = preprocess_pipeline.transform(test_data)\n",
    "y_pred = svm_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7329588014981274"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "svm_scores = cross_val_score(svm_clf, X_train, y_train, cv=10)\n",
    "svm_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8126466916354558"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10)\n",
    "forest_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8876404494382022, 0.8089887640449438)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_scores.shape, forest_scores[:5]\n",
    "forest_scores, svm_scores\n",
    "forest_scores.max(), svm_scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot([1]*10, svm_scores, \".\")\n",
    "plt.plot([2]*10, forest_scores, \".\")\n",
    "plt.boxplot([svm_scores, forest_scores], labels=(\"SVM\", \"Random Forest\"))\n",
    "plt.ylabel(\"Accuracy\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeBucket</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0.362745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>0.423256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45.0</th>\n",
       "      <td>0.404494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Survived\n",
       "AgeBucket          \n",
       "0.0        0.576923\n",
       "15.0       0.362745\n",
       "30.0       0.423256\n",
       "45.0       0.404494\n",
       "60.0       0.240000\n",
       "75.0       1.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AgeBucket\"] = train_data[\"Age\"] // 15 * 15\n",
    "train_data[[\"AgeBucket\", \"Survived\"]].groupby([\"AgeBucket\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      15.0\n",
       "1      30.0\n",
       "2      15.0\n",
       "3      30.0\n",
       "4      30.0\n",
       "       ... \n",
       "886    15.0\n",
       "887    15.0\n",
       "888     NaN\n",
       "889    15.0\n",
       "890    30.0\n",
       "Name: AgeBucket, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"AgeBucket\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelativesOnboard</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Survived\n",
       "RelativesOnboard          \n",
       "0                 0.303538\n",
       "1                 0.552795\n",
       "2                 0.578431\n",
       "3                 0.724138\n",
       "4                 0.200000\n",
       "5                 0.136364\n",
       "6                 0.333333\n",
       "7                 0.000000\n",
       "10                0.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"RelativesOnboard\"] = train_data[\"SibSp\"] + train_data[\"Parch\"]\n",
    "train_data[[\"RelativesOnboard\", \"Survived\"]].groupby(['RelativesOnboard']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6936704119850188"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_scores = cross_val_score(sgd_clf, X_train, y_train, cv=10)\n",
    "sgd_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.    ,  1.    ,  0.    ,  7.25  ,  0.    ,  0.    ,  1.    ,\n",
       "         0.    ,  1.    ,  0.    ,  0.    ,  1.    ],\n",
       "       [38.    ,  1.    ,  0.    , 71.2833,  1.    ,  0.    ,  0.    ,\n",
       "         1.    ,  0.    ,  1.    ,  0.    ,  0.    ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'AgeBucket',\n",
       "       'RelativesOnboard'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  58 tasks      | elapsed:    2.1s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    2.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=SGDClassifier(alpha=0.0001, average=False,\n",
       "                                     class_weight=None, early_stopping=False,\n",
       "                                     epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "                                     l1_ratio=0.15, learning_rate='optimal',\n",
       "                                     loss='hinge', max_iter=1000,\n",
       "                                     n_iter_no_change=5, n_jobs=None,\n",
       "                                     penalty='l2', power_t=0.5,\n",
       "                                     random_state=None, shuffle=True, tol=0.001,\n",
       "                                     validation_fraction=0.1, verbose=0,\n",
       "                                     warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid=[{'alpha': [0.0001, 0.001, 0.01],\n",
       "                          'loss': ['hinge', 'log', 'modified_huber',\n",
       "                                   'squared_hinge', 'perceptron'],\n",
       "                          'penalty': ['l2', 'l1']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=3)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [\n",
    "    {\"loss\":[\"hinge\", \"log\", \"modified_huber\", \"squared_hinge\", \"perceptron\"],\n",
    "     \"penalty\":[\"l2\", \"l1\"],\n",
    "     \"alpha\":[0.0001, 0.001, 0.01],}\n",
    "]\n",
    "\n",
    "sgd_clf = SGDClassifier()\n",
    "grid_search = GridSearchCV(sgd_clf, param_grid, cv=5, n_jobs=-1, verbose=3)\n",
    "grid_search.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.001, 'loss': 'perceptron', 'penalty': 'l1'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7800075324838366"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 102 tasks      | elapsed:    1.3s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    1.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('random_forest',\n",
       "                              RandomForestClassifier(bootstrap=True,\n",
       "                                                     ccp_alpha=0.0,\n",
       "                                                     class_weight=None,\n",
       "                                                     criterion='gini',\n",
       "                                                     max_depth=None,\n",
       "                                                     max_features='auto',\n",
       "                                                     max_leaf_nodes=None,\n",
       "                                                     max_samples=None,\n",
       "                                                     min_impurity_decrease=0.0,\n",
       "                                                     min_impurity_split=None,\n",
       "                                                     min_samples_leaf=1,\n",
       "                                                     min_samples_split=2,\n",
       "                                                     min_weight_fraction_leaf=0.0,\n",
       "                                                     n_estimators=100,\n",
       "                                                     n_jobs=Non...\n",
       "                                           pre_dispatch='2*n_jobs', refit=True,\n",
       "                                           return_train_score=False,\n",
       "                                           scoring=None, verbose=3)),\n",
       "                             ('svm',\n",
       "                              SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                                  class_weight=None, coef0=0.0,\n",
       "                                  decision_function_shape='ovr', degree=3,\n",
       "                                  gamma='auto', kernel='rbf', max_iter=-1,\n",
       "                                  probability=False, random_state=None,\n",
       "                                  shrinking=True, tol=0.001, verbose=False))],\n",
       "                 flatten_transform=True, n_jobs=None, voting='hard',\n",
       "                 weights=None)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "eclf = VotingClassifier(estimators=[\n",
    "    (\"random_forest\", forest_clf),\n",
    "    (\"sgd\", grid_search),\n",
    "    (\"svm\", svm_clf)\n",
    "], voting=\"hard\")\n",
    "\n",
    "eclf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 117 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 133 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 132 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 117 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 132 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 132 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 117 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 132 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 132 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 150 out of 150 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8059051186017477"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf_scores = cross_val_score(eclf, X_train, y_train, cv=10)\n",
    "eclf_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34.5,  0. ,  0. , ...,  0. ,  1. ,  0. ],\n",
       "       [47. ,  1. ,  0. , ...,  0. ,  0. ,  1. ],\n",
       "       [62. ,  0. ,  0. , ...,  0. ,  1. ,  0. ],\n",
       "       ...,\n",
       "       [38.5,  0. ,  0. , ...,  0. ,  0. ,  1. ],\n",
       "       [27. ,  0. ,  0. , ...,  0. ,  0. ,  1. ],\n",
       "       [27. ,  1. ,  1. , ...,  1. ,  0. ,  0. ]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = preprocess_pipeline.fit_transform(test_data)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = eclf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((418,), (418, 12))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_example = load_titanic_data(\"gender_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_example.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(test_data[\"PassengerId\"].unique()) == list(range(892, 1310))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(np.c_[test_data[\"PassengerId\"], y_pred], columns=[\"PassengerId\", \"Survived\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((418, 2), (418, 2))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.shape, result_example.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv(\"titanic_results_majority_voting_v1.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_example.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kaggle accuracy score: 0.72248"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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