competitions/titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extração de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "train = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "test = pd.read_csv('/kaggle/input/titanic/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análise dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Muita informação ausente quanto a _Cabin_ e algumas quanto à _Age_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N/A train\n",
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('N/A train')\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Supondo que \"mulheres e crianças primeiro!\" foi mesmo uma abordagem tomada, podemos avaliar estes detalhes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sb.FacetGrid(train, col='Survived').map(plt.hist, 'Sex', bins=2)\n",
    "train[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.42</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>63.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>53.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.67</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>28.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>36.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>34.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>23.50</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Survived\n",
       "0    0.42       1.0\n",
       "9    5.00       1.0\n",
       "79  63.00       1.0\n",
       "68  53.00       1.0\n",
       "1    0.67       1.0\n",
       "..    ...       ...\n",
       "37  28.50       0.0\n",
       "49  36.50       0.0\n",
       "46  34.50       0.0\n",
       "26  20.50       0.0\n",
       "30  23.50       0.0\n",
       "\n",
       "[88 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.FacetGrid(train, col='Survived').map(plt.hist, 'Age', bins=20)\n",
    "train[[\"Age\", \"Survived\"]].groupby(['Age'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualmente, parece que a maioria das crianças até 5 anos sobreviveu, a maioria dos adultos de 20 a 40 morreu. Revendo esses dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.704545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.397403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.377778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.327217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Survived\n",
       "0    0  0.704545\n",
       "2    2  0.397403\n",
       "1    1  0.377778\n",
       "3    3  0.327217"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = train[['Age', 'Survived']].copy()\n",
    "df['Age'] = df.apply(lambda row: 0 if row.Age <= 5 else 1 if row.Age <= 20 else 2 if row.Age <= 40 else 3, axis=1)\n",
    "sb.FacetGrid(df, col='Survived').map(plt.hist, 'Age', bins=4)\n",
    "df[[\"Age\", \"Survived\"]].groupby(['Age'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos incluir essas novas características nos dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['eh_mulher'] = train.apply(lambda row: 1 if row.Sex =='female' else 0, axis=1)\n",
    "train['eh_crianca'] = train.apply(lambda row: 1 if row.Age <= 5 else 0, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E então pode-se fazer analisar as correlações entre os valores numéricos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f377f52efd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_data = train.drop('PassengerId', axis=1).select_dtypes(include=np.number)\n",
    "sb.heatmap(numeric_data.corr(), annot=True, fmt='.2f',cmap ='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aparentemente, \"ser mulher\" tem correlação direta com a sobrevivência, e o cavalheirismo teria influência... Ser \"criança\", nem tanto, mas isso talvez possa ser devido à grande quantidade de dados ausentes para idade. \n",
    "\n",
    "O valor da passagem parece ser relevante também."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.select_dtypes(include=np.number).drop(['PassengerId', 'Survived'], axis=1)\n",
    "y_train = train['Survived']\n",
    "\n",
    "X_test = test.select_dtypes(include=np.number).drop('PassengerId', axis=1)\n",
    "X_test['eh_mulher'] = test.apply(lambda row: 1 if row.Sex =='female' else 0, axis=1)\n",
    "X_test['eh_crianca'] = test.apply(lambda row: 1 if row.Age <= 5 else 0, axis=1)\n",
    "\n",
    "X_train.fillna(X_train.mean(), inplace=True)\n",
    "X_test.fillna(X_train.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit / Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "model_instances = [DecisionTreeClassifier(), GaussianNB(), LogisticRegression(solver='lbfgs', max_iter=1000), RandomForestClassifier(n_estimators=100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_score(model):\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    return 100 * model.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def cv_score(model, cv=10):\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    return 100 * cross_val_score(model, X_train, y_train, cv=cv, scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {'Model': [type(model).__name__ for model in model_instances],\n",
    "           'Score': [simple_score(model) for model in model_instances],\n",
    "           'CV_Score': [cv_score(model, 10) for model in model_instances]}\n",
    "\n",
    "models_eval = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "    .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",
       "      <th>Score</th>\n",
       "      <th>CV_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>98.204265</td>\n",
       "      <td>76.998752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>98.204265</td>\n",
       "      <td>80.933833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>81.705948</td>\n",
       "      <td>80.806492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>80.134680</td>\n",
       "      <td>78.234707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model      Score   CV_Score\n",
       "0  DecisionTreeClassifier  98.204265  76.998752\n",
       "3  RandomForestClassifier  98.204265  80.933833\n",
       "2      LogisticRegression  81.705948  80.806492\n",
       "1              GaussianNB  80.134680  78.234707"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_eval.sort_values(by='Score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Model</th>\n",
       "      <th>Score</th>\n",
       "      <th>CV_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>98.204265</td>\n",
       "      <td>80.933833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>81.705948</td>\n",
       "      <td>80.806492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>80.134680</td>\n",
       "      <td>78.234707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>98.204265</td>\n",
       "      <td>76.998752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model      Score   CV_Score\n",
       "3  RandomForestClassifier  98.204265  80.933833\n",
       "2      LogisticRegression  81.705948  80.806492\n",
       "1              GaussianNB  80.134680  78.234707\n",
       "0  DecisionTreeClassifier  98.204265  76.998752"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_eval.sort_values(by='CV_Score',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_name = models_eval['Model'][models_eval['CV_Score'].idxmax()]\n",
    "\n",
    "for model in model_instances:\n",
    "    if type(model).__name__ == best_model_name:\n",
    "        y_pred = model.predict(X_test)\n",
    "        break\n",
    "\n",
    "submission = pd.DataFrame({'PassengerId': test['PassengerId'], 'Survived': y_pred})\n",
    "submission.to_csv('submission.csv',index=False)"
   ]
  }
 ],
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