Titanic. RandomForest..ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4e400e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a1daa40c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/kaggle/input/titanic/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "eadffa85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "bddfcc61",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PassengerId'], inplace=True, axis=1)\n",
    "target = 'Survived'\n",
    "num = [x for x in df.columns if df.dtypes[x] in ('int64', 'float')]\n",
    "cat = [x for x in df.columns if df.dtypes[x] == 'object']\n",
    "num.remove(target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a51a3de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(df, test_size=.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e584c0e",
   "metadata": {},
   "source": [
    "# Explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "9d8e0bf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    1.000000\n",
       "Fare        0.229665\n",
       "Parch       0.081027\n",
       "SibSp      -0.047006\n",
       "Age        -0.101539\n",
       "Pclass     -0.314294\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = train.corr(numeric_only=True)\n",
    "corr[target].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6ccd5a2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(712, 11)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ff9ba8a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.fill_diagonal(corr.values, 0)\n",
    "sns.heatmap(corr, annot=True, cmap='RdBu', center=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0e58e10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age         141\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Ticket        0\n",
       "Fare          0\n",
       "Cabin       549\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "15f19a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.744939\n",
       "male      0.191398\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Sex')[target].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "2cccfb11",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>439.0</td>\n",
       "      <td>22.789292</td>\n",
       "      <td>31.810613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.89165</td>\n",
       "      <td>10.5</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>263.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>273.0</td>\n",
       "      <td>46.453923</td>\n",
       "      <td>67.781291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.24170</td>\n",
       "      <td>26.0</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count       mean        std  min       25%   50%      75%       max\n",
       "Survived                                                                     \n",
       "0         439.0  22.789292  31.810613  0.0   7.89165  10.5  26.0000  263.0000\n",
       "1         273.0  46.453923  67.781291  0.0  11.24170  26.0  56.4958  512.3292"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(target)['Fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "40577c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "  <tbody>\n",
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       "      <td>2</td>\n",
       "      <td>Watson, Mr. Ennis Hastings</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Tornquist, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
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       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Frost, Mr. Anthony Wood \"Archie\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239854</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Harrison, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B94</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. William Cahoone Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Leonard, Mr. Lionel</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Mr. Thomas Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112050</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Campbell, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Knight, Mr. Robert J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Parr, Mr. William Henry Marsh</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Parkes, Mr. Francis \"Frank\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass                              Name   Sex   Age  SibSp  \\\n",
       "674         0       2        Watson, Mr. Ennis Hastings  male   NaN      0   \n",
       "597         0       3               Johnson, Mr. Alfred  male  49.0      0   \n",
       "271         1       3      Tornquist, Mr. William Henry  male  25.0      0   \n",
       "481         0       2  Frost, Mr. Anthony Wood \"Archie\"  male   NaN      0   \n",
       "263         0       1             Harrison, Mr. William  male  40.0      0   \n",
       "302         0       3   Johnson, Mr. William Cahoone Jr  male  19.0      0   \n",
       "179         0       3               Leonard, Mr. Lionel  male  36.0      0   \n",
       "806         0       1            Andrews, Mr. Thomas Jr  male  39.0      0   \n",
       "466         0       2             Campbell, Mr. William  male   NaN      0   \n",
       "732         0       2              Knight, Mr. Robert J  male   NaN      0   \n",
       "633         0       1     Parr, Mr. William Henry Marsh  male   NaN      0   \n",
       "277         0       2       Parkes, Mr. Francis \"Frank\"  male   NaN      0   \n",
       "\n",
       "     Parch  Ticket  Fare Cabin Embarked  \n",
       "674      0  239856   0.0   NaN        S  \n",
       "597      0    LINE   0.0   NaN        S  \n",
       "271      0    LINE   0.0   NaN        S  \n",
       "481      0  239854   0.0   NaN        S  \n",
       "263      0  112059   0.0   B94        S  \n",
       "302      0    LINE   0.0   NaN        S  \n",
       "179      0    LINE   0.0   NaN        S  \n",
       "806      0  112050   0.0   A36        S  \n",
       "466      0  239853   0.0   NaN        S  \n",
       "732      0  239855   0.0   NaN        S  \n",
       "633      0  112052   0.0   NaN        S  \n",
       "277      0  239853   0.0   NaN        S  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['Fare'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354760fe",
   "metadata": {},
   "source": [
    "# Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "8e7d7ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class DataSelect(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attributes):\n",
    "        self.attributes = attributes\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        return X[self.attributes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5c94d4c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "1893eed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pp = Pipeline([\n",
    "    ('dataselect', DataSelect(num))\n",
    "    , ('impute', SimpleImputer(strategy='median'))\n",
    "    , ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "cat_pp = Pipeline([\n",
    "    ('dataselect', DataSelect(['Sex']))\n",
    "    , ('encoder', OneHotEncoder(sparse_output=False))\n",
    "])\n",
    "\n",
    "pipe = FeatureUnion([\n",
    "    ('num', num_pp)\n",
    "    , ('cat', cat_pp)\n",
    "])\n",
    "\n",
    "pipe.fit(train)\n",
    "train_pre = pipe.transform(train)\n",
    "test_pre = pipe.transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5baabb77",
   "metadata": {},
   "source": [
    "# Select Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "356f5ac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "79cc1501",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 0.7907318034078596 test: 0.7768253968253969\n"
     ]
    }
   ],
   "source": [
    "cls_log = LogisticRegression()\n",
    "print(\n",
    "    'train:', cross_val_score(cls_log, train_pre, train[target]).mean()\n",
    "    , 'test:', cross_val_score(cls_log, test_pre, test[target]).mean()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "754183f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 0.8286516300600807 test: 0.7987301587301587\n"
     ]
    }
   ],
   "source": [
    "cls_svc = SVC()\n",
    "print(\n",
    "    'train:', cross_val_score(cls_svc, train_pre, train[target]).mean()\n",
    "    , 'test:', cross_val_score(cls_svc, test_pre, test[target]).mean()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c0217ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 0.8019403132079189 test: 0.8158730158730159\n"
     ]
    }
   ],
   "source": [
    "cls_ran = RandomForestClassifier()\n",
    "print(\n",
    "    'train:', cross_val_score(cls_ran, train_pre, train[target]).mean()\n",
    "    , 'test:', cross_val_score(cls_ran, test_pre, test[target]).mean()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "8ec50ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_base = cls_ran"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97df452",
   "metadata": {},
   "source": [
    "# Fine Tuning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "2f1c51db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_base.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "bb61eeb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5, 'n_estimators': 200, 'random_state': 0}"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "params = {\n",
    "    'random_state': [0]\n",
    "    , 'n_estimators': [100, 200, 300]\n",
    "    , 'max_depth': [5, 7, 9]\n",
    "}\n",
    "grid = GridSearchCV(model_base, param_grid=params)\n",
    "grid.fit(train_pre, train[target])\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "f5e68fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_tune = RandomForestClassifier(random_state=0, n_estimators=200, max_depth=5)\n",
    "model_tune.fit(train_pre, train[target]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "596880e1",
   "metadata": {},
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "d54ed369",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "d60b5bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>tr_tune</th>\n",
       "      <th>te_tune</th>\n",
       "      <th>tr_diff</th>\n",
       "      <th>te_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.825843</td>\n",
       "      <td>0.804469</td>\n",
       "      <td>1.730104</td>\n",
       "      <td>-0.689655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.828194</td>\n",
       "      <td>0.793103</td>\n",
       "      <td>7.202755</td>\n",
       "      <td>-1.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.688645</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-4.568528</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1_score</th>\n",
       "      <td>0.752000</td>\n",
       "      <td>0.724409</td>\n",
       "      <td>0.775635</td>\n",
       "      <td>-0.787402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roc_auc_score</th>\n",
       "      <td>0.799903</td>\n",
       "      <td>0.778788</td>\n",
       "      <td>0.648833</td>\n",
       "      <td>-0.580271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tr_tune   te_tune   tr_diff   te_diff\n",
       "accuracy       0.825843  0.804469  1.730104 -0.689655\n",
       "precision      0.828194  0.793103  7.202755 -1.724138\n",
       "recall         0.688645  0.666667 -4.568528  0.000000\n",
       "f1_score       0.752000  0.724409  0.775635 -0.787402\n",
       "roc_auc_score  0.799903  0.778788  0.648833 -0.580271"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def getScores(m, X, y):\n",
    "    prediction = cross_val_predict(m, X, y)\n",
    "    return {\n",
    "        'accuracy': accuracy_score(y, prediction)\n",
    "        , 'precision': precision_score(y, prediction)\n",
    "        , 'recall': recall_score(y, prediction)\n",
    "        , 'f1_score': f1_score(y, prediction)\n",
    "        , 'roc_auc_score': roc_auc_score(y, prediction)\n",
    "    }\n",
    "\n",
    "scores = pd.DataFrame({\n",
    "    'tr_base': getScores(model_base, train_pre, train[target])\n",
    "    , 'tr_tune': getScores(model_tune, train_pre, train[target])\n",
    "    , 'te_base': getScores(model_base, test_pre, test[target])\n",
    "    , 'te_tune': getScores(model_tune, test_pre, test[target])\n",
    "})\n",
    "\n",
    "scores['tr_diff'] = (scores['tr_tune'] / scores['tr_base'] -1) * 100\n",
    "scores['te_diff'] = (scores['te_tune'] / scores['te_base'] -1) * 100\n",
    "scores.drop(['tr_base', 'te_base'], axis=1, inplace=True)\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3362c19",
   "metadata": {},
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "cff8d49a",
   "metadata": {},
   "outputs": [],
   "source": [
    "samp = pd.read_csv('/kaggle/input/titanic/test.csv')\n",
    "\n",
    "pd.DataFrame({\n",
    "    'PassengerId': samp['PassengerId']\n",
    "    , 'Survived': model_tune.predict(pipe.transform(samp))\n",
    "}).to_csv('submission.csv', index=False)"
   ]
  }
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