titanic-pv-ver1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3dad8e20",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:12.525612Z",
     "iopub.status.busy": "2024-08-03T04:39:12.524695Z",
     "iopub.status.idle": "2024-08-03T04:39:13.440712Z",
     "shell.execute_reply": "2024-08-03T04:39:13.439192Z"
    },
    "papermill": {
     "duration": 0.948708,
     "end_time": "2024-08-03T04:39:13.444474",
     "exception": false,
     "start_time": "2024-08-03T04:39:12.495766",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/titanic/train.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/gender_submission.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64a12b52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:13.519257Z",
     "iopub.status.busy": "2024-08-03T04:39:13.517980Z",
     "iopub.status.idle": "2024-08-03T04:39:15.660430Z",
     "shell.execute_reply": "2024-08-03T04:39:15.659129Z"
    },
    "papermill": {
     "duration": 2.177094,
     "end_time": "2024-08-03T04:39:15.663201",
     "exception": false,
     "start_time": "2024-08-03T04:39:13.486107",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, classification_report\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from warnings import simplefilter\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "simplefilter(\"ignore\", category=ConvergenceWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4952fb4",
   "metadata": {
    "papermill": {
     "duration": 0.025353,
     "end_time": "2024-08-03T04:39:15.714405",
     "exception": false,
     "start_time": "2024-08-03T04:39:15.689052",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Round 1 - Simple Preprocessing & Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a5b6b3",
   "metadata": {
    "papermill": {
     "duration": 0.025127,
     "end_time": "2024-08-03T04:39:15.767100",
     "exception": false,
     "start_time": "2024-08-03T04:39:15.741973",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea68672d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:15.821197Z",
     "iopub.status.busy": "2024-08-03T04:39:15.820355Z",
     "iopub.status.idle": "2024-08-03T04:39:15.857034Z",
     "shell.execute_reply": "2024-08-03T04:39:15.855843Z"
    },
    "papermill": {
     "duration": 0.066569,
     "end_time": "2024-08-03T04:39:15.859752",
     "exception": false,
     "start_time": "2024-08-03T04:39:15.793183",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv('/kaggle/input/titanic/test.csv')\n",
    "train = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2e5c580",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:15.912768Z",
     "iopub.status.busy": "2024-08-03T04:39:15.912387Z",
     "iopub.status.idle": "2024-08-03T04:39:15.946947Z",
     "shell.execute_reply": "2024-08-03T04:39:15.945106Z"
    },
    "papermill": {
     "duration": 0.064705,
     "end_time": "2024-08-03T04:39:15.950272",
     "exception": false,
     "start_time": "2024-08-03T04:39:15.885567",
     "status": "completed"
    },
    "tags": []
   },
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info() # missing value in 'Age', 'Cabin', 'Embarked'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "de085b34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:16.003960Z",
     "iopub.status.busy": "2024-08-03T04:39:16.003512Z",
     "iopub.status.idle": "2024-08-03T04:39:16.347059Z",
     "shell.execute_reply": "2024-08-03T04:39:16.345087Z"
    },
    "papermill": {
     "duration": 0.374753,
     "end_time": "2024-08-03T04:39:16.351097",
     "exception": false,
     "start_time": "2024-08-03T04:39:15.976344",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age Distribution')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "train['Age'].hist(bins=20)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Age Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5bd0d05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:16.482915Z",
     "iopub.status.busy": "2024-08-03T04:39:16.482473Z",
     "iopub.status.idle": "2024-08-03T04:39:16.492014Z",
     "shell.execute_reply": "2024-08-03T04:39:16.490591Z"
    },
    "papermill": {
     "duration": 0.03955,
     "end_time": "2024-08-03T04:39:16.494326",
     "exception": false,
     "start_time": "2024-08-03T04:39:16.454776",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median age =  28.0\n",
      "Mean age =  29.69911764705882\n"
     ]
    }
   ],
   "source": [
    "print('Median age = ', train['Age'].median())\n",
    "print('Mean age = ', train['Age'].mean())\n",
    "# A little bit of right skew"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94088e4a",
   "metadata": {
    "papermill": {
     "duration": 0.038718,
     "end_time": "2024-08-03T04:39:16.565516",
     "exception": false,
     "start_time": "2024-08-03T04:39:16.526798",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Fill na values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a027346",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:16.642175Z",
     "iopub.status.busy": "2024-08-03T04:39:16.641735Z",
     "iopub.status.idle": "2024-08-03T04:39:16.649938Z",
     "shell.execute_reply": "2024-08-03T04:39:16.648613Z"
    },
    "papermill": {
     "duration": 0.046788,
     "end_time": "2024-08-03T04:39:16.652397",
     "exception": false,
     "start_time": "2024-08-03T04:39:16.605609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/486169235.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  train['Age'].fillna(train['Age'].median(), inplace=True)\n"
     ]
    }
   ],
   "source": [
    "train['Age'].fillna(train['Age'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96df83e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:16.707934Z",
     "iopub.status.busy": "2024-08-03T04:39:16.706877Z",
     "iopub.status.idle": "2024-08-03T04:39:16.716256Z",
     "shell.execute_reply": "2024-08-03T04:39:16.714466Z"
    },
    "papermill": {
     "duration": 0.041046,
     "end_time": "2024-08-03T04:39:16.720212",
     "exception": false,
     "start_time": "2024-08-03T04:39:16.679166",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99e63021",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:16.780266Z",
     "iopub.status.busy": "2024-08-03T04:39:16.779522Z",
     "iopub.status.idle": "2024-08-03T04:39:16.801820Z",
     "shell.execute_reply": "2024-08-03T04:39:16.800482Z"
    },
    "papermill": {
     "duration": 0.053444,
     "end_time": "2024-08-03T04:39:16.804543",
     "exception": false,
     "start_time": "2024-08-03T04:39:16.751099",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "      <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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8b467e",
   "metadata": {
    "papermill": {
     "duration": 0.027008,
     "end_time": "2024-08-03T04:39:16.858628",
     "exception": false,
     "start_time": "2024-08-03T04:39:16.831620",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Drop some attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e74a7fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:16.914221Z",
     "iopub.status.busy": "2024-08-03T04:39:16.913760Z",
     "iopub.status.idle": "2024-08-03T04:39:16.921758Z",
     "shell.execute_reply": "2024-08-03T04:39:16.920332Z"
    },
    "papermill": {
     "duration": 0.039151,
     "end_time": "2024-08-03T04:39:16.924730",
     "exception": false,
     "start_time": "2024-08-03T04:39:16.885579",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in 'Cabin' =  687 ( 77.1 % of datapoints)\n"
     ]
    }
   ],
   "source": [
    "cabin_na =  train['Cabin'].isna().sum()\n",
    "print('Missing values in \\'Cabin\\' = ', cabin_na, '(', round(cabin_na*100/len(train),2), '% of datapoints)') # too many missing values in 'Cabin' -> drop 'Cabin' \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ee432437",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:16.981923Z",
     "iopub.status.busy": "2024-08-03T04:39:16.981518Z",
     "iopub.status.idle": "2024-08-03T04:39:16.991595Z",
     "shell.execute_reply": "2024-08-03T04:39:16.990155Z"
    },
    "papermill": {
     "duration": 0.041613,
     "end_time": "2024-08-03T04:39:16.994125",
     "exception": false,
     "start_time": "2024-08-03T04:39:16.952512",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.drop(columns='Cabin', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c3dd3b19",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.050602Z",
     "iopub.status.busy": "2024-08-03T04:39:17.050166Z",
     "iopub.status.idle": "2024-08-03T04:39:17.063787Z",
     "shell.execute_reply": "2024-08-03T04:39:17.062545Z"
    },
    "papermill": {
     "duration": 0.045835,
     "end_time": "2024-08-03T04:39:17.067140",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.021305",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 76.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4dab0ae9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.124718Z",
     "iopub.status.busy": "2024-08-03T04:39:17.123618Z",
     "iopub.status.idle": "2024-08-03T04:39:17.133217Z",
     "shell.execute_reply": "2024-08-03T04:39:17.132046Z"
    },
    "papermill": {
     "duration": 0.040951,
     "end_time": "2024-08-03T04:39:17.135476",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.094525",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c3fbbeea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.191688Z",
     "iopub.status.busy": "2024-08-03T04:39:17.191292Z",
     "iopub.status.idle": "2024-08-03T04:39:17.202039Z",
     "shell.execute_reply": "2024-08-03T04:39:17.200752Z"
    },
    "papermill": {
     "duration": 0.041706,
     "end_time": "2024-08-03T04:39:17.204415",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.162709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticket\n",
       "347082      7\n",
       "CA. 2343    7\n",
       "1601        7\n",
       "3101295     6\n",
       "CA 2144     6\n",
       "           ..\n",
       "9234        1\n",
       "19988       1\n",
       "2693        1\n",
       "PC 17612    1\n",
       "370376      1\n",
       "Name: count, Length: 681, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Ticket'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2a67942a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.261245Z",
     "iopub.status.busy": "2024-08-03T04:39:17.260809Z",
     "iopub.status.idle": "2024-08-03T04:39:17.267482Z",
     "shell.execute_reply": "2024-08-03T04:39:17.266321Z"
    },
    "papermill": {
     "duration": 0.038299,
     "end_time": "2024-08-03T04:39:17.269874",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.231575",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.drop(columns='Ticket', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ff06553",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.326379Z",
     "iopub.status.busy": "2024-08-03T04:39:17.325975Z",
     "iopub.status.idle": "2024-08-03T04:39:17.332465Z",
     "shell.execute_reply": "2024-08-03T04:39:17.331295Z"
    },
    "papermill": {
     "duration": 0.037295,
     "end_time": "2024-08-03T04:39:17.334701",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.297406",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.drop(columns='PassengerId', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "37f32ca9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.392456Z",
     "iopub.status.busy": "2024-08-03T04:39:17.391536Z",
     "iopub.status.idle": "2024-08-03T04:39:17.399106Z",
     "shell.execute_reply": "2024-08-03T04:39:17.398043Z"
    },
    "papermill": {
     "duration": 0.038979,
     "end_time": "2024-08-03T04:39:17.401297",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.362318",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train.drop(columns='Name', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ce9d921c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.457136Z",
     "iopub.status.busy": "2024-08-03T04:39:17.456733Z",
     "iopub.status.idle": "2024-08-03T04:39:17.471218Z",
     "shell.execute_reply": "2024-08-03T04:39:17.469947Z"
    },
    "papermill": {
     "duration": 0.044948,
     "end_time": "2024-08-03T04:39:17.473383",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.428435",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba6ba99",
   "metadata": {
    "papermill": {
     "duration": 0.027239,
     "end_time": "2024-08-03T04:39:17.528280",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.501041",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Summary :\n",
    "- 'Cabin' should be dropped because there are too many missing values.\n",
    "- 'Ticket', 'Name', 'PassengerId' are not neccessary for data analysis and data modelling. So, we can drop these features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfa7ad34",
   "metadata": {
    "papermill": {
     "duration": 0.027702,
     "end_time": "2024-08-03T04:39:17.584138",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.556436",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Convert text, catagotical data to numerical (One-Hot-Encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "db89482d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.640944Z",
     "iopub.status.busy": "2024-08-03T04:39:17.640536Z",
     "iopub.status.idle": "2024-08-03T04:39:17.649277Z",
     "shell.execute_reply": "2024-08-03T04:39:17.647998Z"
    },
    "papermill": {
     "duration": 0.040062,
     "end_time": "2024-08-03T04:39:17.651715",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.611653",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# One-Hot-Encoding (Dummy variable)\n",
    "dummies_sex = pd.get_dummies(train['Sex'])\n",
    "dummies_embarked = pd.get_dummies(train['Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d5433fa2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.708648Z",
     "iopub.status.busy": "2024-08-03T04:39:17.708239Z",
     "iopub.status.idle": "2024-08-03T04:39:17.719172Z",
     "shell.execute_reply": "2024-08-03T04:39:17.718128Z"
    },
    "papermill": {
     "duration": 0.042184,
     "end_time": "2024-08-03T04:39:17.721587",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.679403",
     "status": "completed"
    },
    "tags": []
   },
   "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>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       C      Q      S\n",
       "0  False  False   True\n",
       "1   True  False  False\n",
       "2  False  False   True\n",
       "3  False  False   True\n",
       "4  False  False   True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies_embarked.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "98640e7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.780416Z",
     "iopub.status.busy": "2024-08-03T04:39:17.779998Z",
     "iopub.status.idle": "2024-08-03T04:39:17.794021Z",
     "shell.execute_reply": "2024-08-03T04:39:17.792676Z"
    },
    "papermill": {
     "duration": 0.046593,
     "end_time": "2024-08-03T04:39:17.796370",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.749777",
     "status": "completed"
    },
    "tags": []
   },
   "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>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         C      Q      S\n",
       "61   False  False  False\n",
       "829  False  False  False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies_embarked[(dummies_embarked['C'] == False)*(dummies_embarked['Q'] == False)*(dummies_embarked['S'] == False)] # show N/A values in embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "992e6bec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.855408Z",
     "iopub.status.busy": "2024-08-03T04:39:17.855025Z",
     "iopub.status.idle": "2024-08-03T04:39:17.862155Z",
     "shell.execute_reply": "2024-08-03T04:39:17.860986Z"
    },
    "papermill": {
     "duration": 0.038766,
     "end_time": "2024-08-03T04:39:17.864451",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.825685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_onehot = pd.concat([train, dummies_sex, dummies_embarked], axis=1)\n",
    "train_onehot.drop(columns=['Sex', 'Embarked'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9cd0d3e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:17.924241Z",
     "iopub.status.busy": "2024-08-03T04:39:17.923814Z",
     "iopub.status.idle": "2024-08-03T04:39:17.939988Z",
     "shell.execute_reply": "2024-08-03T04:39:17.938962Z"
    },
    "papermill": {
     "duration": 0.0487,
     "end_time": "2024-08-03T04:39:17.942296",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.893596",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  female   male      C      Q  \\\n",
       "0         0       3  22.0      1      0   7.2500   False   True  False  False   \n",
       "1         1       1  38.0      1      0  71.2833    True  False   True  False   \n",
       "2         1       3  26.0      0      0   7.9250    True  False  False  False   \n",
       "3         1       1  35.0      1      0  53.1000    True  False  False  False   \n",
       "4         0       3  35.0      0      0   8.0500   False   True  False  False   \n",
       "\n",
       "       S  \n",
       "0   True  \n",
       "1  False  \n",
       "2   True  \n",
       "3   True  \n",
       "4   True  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358b8c09",
   "metadata": {
    "papermill": {
     "duration": 0.027897,
     "end_time": "2024-08-03T04:39:17.998530",
     "exception": false,
     "start_time": "2024-08-03T04:39:17.970633",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training a Simple Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0fe3537c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:18.057548Z",
     "iopub.status.busy": "2024-08-03T04:39:18.057169Z",
     "iopub.status.idle": "2024-08-03T04:39:18.251071Z",
     "shell.execute_reply": "2024-08-03T04:39:18.249978Z"
    },
    "papermill": {
     "duration": 0.226655,
     "end_time": "2024-08-03T04:39:18.253932",
     "exception": false,
     "start_time": "2024-08-03T04:39:18.027277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.80904523, 0.75879397, 0.81313131])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = train_onehot['Survived']\n",
    "X = train_onehot.iloc[:, 1:]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.33)\n",
    "\n",
    "log_model1 = LogisticRegression(random_state=42, max_iter=300).fit(X_train, y_train)\n",
    "cross_val_score(log_model1, X_train, y_train, scoring='accuracy', cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "07b8f25d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:18.313044Z",
     "iopub.status.busy": "2024-08-03T04:39:18.312610Z",
     "iopub.status.idle": "2024-08-03T04:39:19.395784Z",
     "shell.execute_reply": "2024-08-03T04:39:19.394478Z"
    },
    "papermill": {
     "duration": 1.115919,
     "end_time": "2024-08-03T04:39:19.398426",
     "exception": false,
     "start_time": "2024-08-03T04:39:18.282507",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter =  100 cross_val_score =  [0.81407035 0.7638191  0.81818182]\n",
      "iter =  200 cross_val_score =  [0.81407035 0.75879397 0.81313131]\n",
      "iter =  500 cross_val_score =  [0.80904523 0.75879397 0.81313131]\n",
      "iter =  1000 cross_val_score =  [0.80904523 0.75879397 0.81313131]\n",
      "iter =  2000 cross_val_score =  [0.80904523 0.75879397 0.81313131]\n",
      "iter =  5000 cross_val_score =  [0.80904523 0.75879397 0.81313131]\n",
      "iter =  10000 cross_val_score =  [0.80904523 0.75879397 0.81313131]\n"
     ]
    }
   ],
   "source": [
    "for max_iter_sample in [100, 200, 500, 1000, 2000, 5000, 10000]:\n",
    "    log_model1 = LogisticRegression(random_state=42, max_iter=max_iter_sample).fit(X_train, y_train)\n",
    "    print('iter = ', max_iter_sample, 'cross_val_score = ', cross_val_score(log_model1, X_train, y_train, scoring='accuracy', cv=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d5b42318",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:19.460098Z",
     "iopub.status.busy": "2024-08-03T04:39:19.459681Z",
     "iopub.status.idle": "2024-08-03T04:39:19.611510Z",
     "shell.execute_reply": "2024-08-03T04:39:19.610223Z"
    },
    "papermill": {
     "duration": 0.184918,
     "end_time": "2024-08-03T04:39:19.614302",
     "exception": false,
     "start_time": "2024-08-03T04:39:19.429384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "log_model1 = LogisticRegression(random_state=42, max_iter=100).fit(X_train, y_train) # sklearn uses '1' as a positive class (survived class)\n",
    "acc_model1 = cross_val_score(log_model1, X_train, y_train, scoring='accuracy', cv=3)\n",
    "#print(acc_model1)\n",
    "y_train_pred_model1 = cross_val_predict(log_model1, X_train, y_train, cv=3, method='predict')\n",
    "#print(y_train_pred_model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "397e6146",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:19.673594Z",
     "iopub.status.busy": "2024-08-03T04:39:19.673220Z",
     "iopub.status.idle": "2024-08-03T04:39:19.920291Z",
     "shell.execute_reply": "2024-08-03T04:39:19.919131Z"
    },
    "papermill": {
     "duration": 0.279634,
     "end_time": "2024-08-03T04:39:19.922793",
     "exception": false,
     "start_time": "2024-08-03T04:39:19.643159",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7ad060c2c820>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_train, y_train_pred_model1, labels=[1,0])\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix = cm)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6676e2ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:19.983922Z",
     "iopub.status.busy": "2024-08-03T04:39:19.983506Z",
     "iopub.status.idle": "2024-08-03T04:39:20.056648Z",
     "shell.execute_reply": "2024-08-03T04:39:20.055365Z"
    },
    "papermill": {
     "duration": 0.106753,
     "end_time": "2024-08-03T04:39:20.059107",
     "exception": false,
     "start_time": "2024-08-03T04:39:19.952354",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision =  0.755\n",
      "recall =  0.68\n",
      "f1-score =  0.716\n",
      "accuracy =  0.799\n"
     ]
    }
   ],
   "source": [
    "precision = 151/(151+49)\n",
    "recall = 151/(151+71)\n",
    "f1 = 2*precision*recall/(precision+recall)\n",
    "print('precision = ', round(precision,3))\n",
    "print('recall = ', round(recall,3))\n",
    "print('f1-score = ', round(f1,3))\n",
    "print('accuracy = ', round(cross_val_score(log_model1, X_train, y_train, scoring='accuracy', cv=3).mean(),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aad10ac1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:20.120672Z",
     "iopub.status.busy": "2024-08-03T04:39:20.120271Z",
     "iopub.status.idle": "2024-08-03T04:39:20.135201Z",
     "shell.execute_reply": "2024-08-03T04:39:20.133823Z"
    },
    "papermill": {
     "duration": 0.048132,
     "end_time": "2024-08-03T04:39:20.137599",
     "exception": false,
     "start_time": "2024-08-03T04:39:20.089467",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.87      0.84       374\n",
      "           1       0.76      0.68      0.72       222\n",
      "\n",
      "    accuracy                           0.80       596\n",
      "   macro avg       0.79      0.77      0.78       596\n",
      "weighted avg       0.80      0.80      0.80       596\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report1 = classification_report(y_train, y_train_pred_model1)\n",
    "print(report1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1f625881",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:20.200145Z",
     "iopub.status.busy": "2024-08-03T04:39:20.199286Z",
     "iopub.status.idle": "2024-08-03T04:39:20.216721Z",
     "shell.execute_reply": "2024-08-03T04:39:20.215298Z"
    },
    "papermill": {
     "duration": 0.051717,
     "end_time": "2024-08-03T04:39:20.219456",
     "exception": false,
     "start_time": "2024-08-03T04:39:20.167739",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.87      0.85       175\n",
      "           1       0.80      0.72      0.76       120\n",
      "\n",
      "    accuracy                           0.81       295\n",
      "   macro avg       0.81      0.80      0.80       295\n",
      "weighted avg       0.81      0.81      0.81       295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_test_pred_model1 = log_model1.predict(X_test)\n",
    "report1_test = classification_report(y_test, y_test_pred_model1)\n",
    "print(report1_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462c95e6",
   "metadata": {
    "papermill": {
     "duration": 0.030553,
     "end_time": "2024-08-03T04:39:20.280028",
     "exception": false,
     "start_time": "2024-08-03T04:39:20.249475",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3244e2b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:20.342835Z",
     "iopub.status.busy": "2024-08-03T04:39:20.341736Z",
     "iopub.status.idle": "2024-08-03T04:39:20.597947Z",
     "shell.execute_reply": "2024-08-03T04:39:20.596948Z"
    },
    "papermill": {
     "duration": 0.290026,
     "end_time": "2024-08-03T04:39:20.600504",
     "exception": false,
     "start_time": "2024-08-03T04:39:20.310478",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rf1 = RandomForestClassifier(random_state=42).fit(X_train, y_train)\n",
    "y_train_pred_rf1 = rf1.predict(X_train)\n",
    "y_test_pred_rf1 = rf1.predict(X_test)\n",
    "report_rf1 = classification_report(y_train, y_train_pred_rf1)\n",
    "report_rf1_test = classification_report(y_test, y_test_pred_rf1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "59507ef9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:20.661934Z",
     "iopub.status.busy": "2024-08-03T04:39:20.660776Z",
     "iopub.status.idle": "2024-08-03T04:39:20.667152Z",
     "shell.execute_reply": "2024-08-03T04:39:20.665804Z"
    },
    "papermill": {
     "duration": 0.039534,
     "end_time": "2024-08-03T04:39:20.669694",
     "exception": false,
     "start_time": "2024-08-03T04:39:20.630160",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98       374\n",
      "           1       0.99      0.96      0.97       222\n",
      "\n",
      "    accuracy                           0.98       596\n",
      "   macro avg       0.98      0.98      0.98       596\n",
      "weighted avg       0.98      0.98      0.98       596\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.82      0.81       175\n",
      "           1       0.72      0.69      0.71       120\n",
      "\n",
      "    accuracy                           0.77       295\n",
      "   macro avg       0.76      0.75      0.76       295\n",
      "weighted avg       0.76      0.77      0.77       295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report_rf1)\n",
    "print(report_rf1_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819e2d3f",
   "metadata": {
    "papermill": {
     "duration": 0.029107,
     "end_time": "2024-08-03T04:39:20.728353",
     "exception": false,
     "start_time": "2024-08-03T04:39:20.699246",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Checking imbalance in dataset\n",
    "\n",
    "Accordingly, in Foundations of Imbalanced Learning Gary M. Weiss writes:\n",
    "\n",
    "**There is no agreement, or standard, concerning the exact degree of class imbalance required for a data set to be considered truly \"imbalanced.\"** But most practitioners would certainly agree that a data set where the most common class is less than twice as common as the rarest class would only be marginally unbalanced, that data sets with the imbalance ratio about **10:1 would be modestly imbalanced**, and data sets with imbalance ratios above **1000:1 would be extremely unbalanced**. But ultimately what we care about is how the imbalance impacts learning, and, in particular, the ability to learn the rare classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "de05fe8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:20.790102Z",
     "iopub.status.busy": "2024-08-03T04:39:20.789696Z",
     "iopub.status.idle": "2024-08-03T04:39:20.962822Z",
     "shell.execute_reply": "2024-08-03T04:39:20.961556Z"
    },
    "papermill": {
     "duration": 0.207137,
     "end_time": "2024-08-03T04:39:20.965419",
     "exception": false,
     "start_time": "2024-08-03T04:39:20.758282",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGdCAYAAAD0e7I1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAsb0lEQVR4nO3deXQUZb7/8U8Skk4gdGKApJNLWEQFgoRdaBcue4CIcIyjKMMyw4WRE1DJHWXiRVkcDeKCM16E65kRdA65Oi4wygjIvkhEzcgeUBAvOKQTBiQNKAGS5/eHh/rZEhBCQjeP79c5dU6qnqee+haHCh+qnq4OM8YYAQAAWCo82AUAAADUJsIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqdYJdQCiorKzUwYMHVb9+fYWFhQW7HAAAcBGMMTp27JhSUlIUHn7++zeEHUkHDx5UampqsMsAAADVcODAATVu3Pi87YQdSfXr15f0/R+W2+0OcjUAAOBi+P1+paamOv+Onw9hR3IeXbndbsIOAABXmZ+agsIEZQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACr1Ql2AT8X06ZNC3YJQEiaMmVKsEsAYDnu7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVghp25syZo/T0dLndbrndbnm9Xi1ZssRp79Gjh8LCwgKW+++/P2CM/fv3KzMzU3Xr1lViYqIefvhhnTlz5kqfCgAACFF1gnnwxo0ba8aMGbr++utljNGrr76qwYMH67PPPlObNm0kSWPGjNH06dOdferWrev8XFFRoczMTHk8Hm3cuFHFxcUaMWKEIiMj9dRTT13x8wEAAKEnqGFn0KBBAetPPvmk5syZo48++sgJO3Xr1pXH46ly/w8++EA7d+7UihUrlJSUpPbt2+uJJ57QpEmTNHXqVEVFRdX6OQAAgNAWMnN2Kioq9Prrr+vEiRPyer3O9gULFqhhw4a68cYblZubq2+//dZpKygoUNu2bZWUlORsy8jIkN/v144dO857rPLycvn9/oAFAADYKah3diRp27Zt8nq9OnnypGJjY7Vw4UKlpaVJku677z41bdpUKSkp2rp1qyZNmqTdu3frnXfekST5fL6AoCPJWff5fOc9Zl5enqZNm1ZLZwQAAEJJ0MNOy5YttXnzZpWVlemtt97SyJEjtXbtWqWlpWns2LFOv7Zt2yo5OVm9e/fW3r171aJFi2ofMzc3Vzk5Oc663+9XamrqZZ0HAAAITUF/jBUVFaXrrrtOnTp1Ul5entq1a6c//OEPVfbt2rWrJGnPnj2SJI/Ho5KSkoA+Z9fPN89Hklwul/MJsLMLAACwU9DDzo9VVlaqvLy8yrbNmzdLkpKTkyVJXq9X27ZtU2lpqdNn+fLlcrvdzqMwAADw8xbUx1i5ubkaMGCAmjRpomPHjik/P19r1qzRsmXLtHfvXuXn52vgwIFq0KCBtm7dqokTJ6p79+5KT0+XJPXr109paWkaPny4Zs6cKZ/Pp8mTJys7O1sulyuYpwYAAEJEUMNOaWmpRowYoeLiYsXFxSk9PV3Lli1T3759deDAAa1YsUIvvPCCTpw4odTUVGVlZWny5MnO/hEREVq8eLHGjRsnr9erevXqaeTIkQHv5QEAAD9vQQ07f/7zn8/blpqaqrVr1/7kGE2bNtX7779fk2UBAACLhNycHQAAgJpE2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1YIadubMmaP09HS53W653W55vV4tWbLEaT958qSys7PVoEEDxcbGKisrSyUlJQFj7N+/X5mZmapbt64SExP18MMP68yZM1f6VAAAQIgKathp3LixZsyYocLCQn366afq1auXBg8erB07dkiSJk6cqPfee09vvvmm1q5dq4MHD+rOO+909q+oqFBmZqZOnTqljRs36tVXX9X8+fP1+OOPB+uUAABAiAkzxphgF/FDCQkJeuaZZ3TXXXepUaNGys/P11133SVJ2rVrl1q3bq2CggJ169ZNS5Ys0e23366DBw8qKSlJkjR37lxNmjRJhw4dUlRU1EUd0+/3Ky4uTmVlZXK73bVyXtOmTauVcYGr3ZQpU4JdAoCr1MX++x0yc3YqKir0+uuv68SJE/J6vSosLNTp06fVp08fp0+rVq3UpEkTFRQUSJIKCgrUtm1bJ+hIUkZGhvx+v3N3qCrl5eXy+/0BCwAAsFPQw862bdsUGxsrl8ul+++/XwsXLlRaWpp8Pp+ioqIUHx8f0D8pKUk+n0+S5PP5AoLO2fazbeeTl5enuLg4Z0lNTa3ZkwIAACEj6GGnZcuW2rx5szZt2qRx48Zp5MiR2rlzZ60eMzc3V2VlZc5y4MCBWj0eAAAInjrBLiAqKkrXXXedJKlTp0765JNP9Ic//EH33HOPTp06paNHjwbc3SkpKZHH45EkeTweffzxxwHjnf201tk+VXG5XHK5XDV8JgAAIBQF/c7Oj1VWVqq8vFydOnVSZGSkVq5c6bTt3r1b+/fvl9frlSR5vV5t27ZNpaWlTp/ly5fL7XYrLS3titcOAABCT1Dv7OTm5mrAgAFq0qSJjh07pvz8fK1Zs0bLli1TXFycRo8erZycHCUkJMjtdmvChAnyer3q1q2bJKlfv35KS0vT8OHDNXPmTPl8Pk2ePFnZ2dncuQEAAJKCHHZKS0s1YsQIFRcXKy4uTunp6Vq2bJn69u0rSZo1a5bCw8OVlZWl8vJyZWRk6KWXXnL2j4iI0OLFizVu3Dh5vV7Vq1dPI0eO1PTp04N1SgAAIMSE3Ht2goH37ADBw3t2AFTXVfeeHQAAgNpA2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1YIadvLy8tSlSxfVr19fiYmJGjJkiHbv3h3Qp0ePHgoLCwtY7r///oA++/fvV2ZmpurWravExEQ9/PDDOnPmzJU8FQAAEKLqBPPga9euVXZ2trp06aIzZ87o0UcfVb9+/bRz507Vq1fP6TdmzBhNnz7dWa9bt67zc0VFhTIzM+XxeLRx40YVFxdrxIgRioyM1FNPPXVFzwcAAISeoIadpUuXBqzPnz9fiYmJKiwsVPfu3Z3tdevWlcfjqXKMDz74QDt37tSKFSuUlJSk9u3b64knntCkSZM0depURUVF1eo5AACA0BZSc3bKysokSQkJCQHbFyxYoIYNG+rGG29Ubm6uvv32W6etoKBAbdu2VVJSkrMtIyNDfr9fO3bsqPI45eXl8vv9AQsAALBTUO/s/FBlZaUeeugh3XLLLbrxxhud7ffdd5+aNm2qlJQUbd26VZMmTdLu3bv1zjvvSJJ8Pl9A0JHkrPt8viqPlZeXp2nTptXSmQAAgFASMmEnOztb27dv14YNGwK2jx071vm5bdu2Sk5OVu/evbV37161aNGiWsfKzc1VTk6Os+73+5Wamlq9wgEAQEgLicdY48eP1+LFi7V69Wo1btz4gn27du0qSdqzZ48kyePxqKSkJKDP2fXzzfNxuVxyu90BCwAAsFNQw44xRuPHj9fChQu1atUqNW/e/Cf32bx5syQpOTlZkuT1erVt2zaVlpY6fZYvXy632620tLRaqRsAAFw9gvoYKzs7W/n5+frb3/6m+vXrO3Ns4uLiFBMTo7179yo/P18DBw5UgwYNtHXrVk2cOFHdu3dXenq6JKlfv35KS0vT8OHDNXPmTPl8Pk2ePFnZ2dlyuVzBPD0AABACgnpnZ86cOSorK1OPHj2UnJzsLG+88YYkKSoqSitWrFC/fv3UqlUr/ed//qeysrL03nvvOWNERERo8eLFioiIkNfr1S9/+UuNGDEi4L08AADg5yuod3aMMRdsT01N1dq1a39ynKZNm+r999+vqbIAAIBFQmKCMgAAQG0h7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDV6gS7AAC42l0z8ZpglwCEpG9mfRPsEiRxZwcAAFiOsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNWqFXauvfZaHT58+JztR48e1bXXXnvZRQEAANSUaoWdr776ShUVFedsLy8v1z//+c/LLgoAAKCmXNJ7dt59913n52XLlikuLs5Zr6io0MqVK9WsWbMaKw4AAOByXVLYGTJkiCQpLCxMI0eODGiLjIxUs2bN9Nxzz9VYcQAAAJfrksJOZWWlJKl58+b65JNP1LBhw1opCgAAoKZU6+si9u3bV9N1AAAA1IpqfzfWypUrtXLlSpWWljp3fM565ZVXLrswAACAmlCtsDNt2jRNnz5dnTt3VnJyssLCwmq6LgAAgBpRrbAzd+5czZ8/X8OHD6/pegAAAGpUtd6zc+rUKd188801XQsAAECNq1bY+Y//+A/l5+fXdC0AAAA1rlph5+TJk3r++ef17//+75owYYJycnIClouVl5enLl26qH79+kpMTNSQIUO0e/fuc46VnZ2tBg0aKDY2VllZWSopKQnos3//fmVmZqpu3bpKTEzUww8/rDNnzlTn1AAAgGWqNWdn69atat++vSRp+/btAW2XMll57dq1ys7OVpcuXXTmzBk9+uij6tevn3bu3Kl69epJkiZOnKi///3vevPNNxUXF6fx48frzjvv1Icffijp+zc3Z2ZmyuPxaOPGjSouLtaIESMUGRmpp556qjqnBwAALBJmjDHBLuKsQ4cOKTExUWvXrlX37t1VVlamRo0aKT8/X3fddZckadeuXWrdurUKCgrUrVs3LVmyRLfffrsOHjyopKQkSd9PoJ40aZIOHTqkqKionzyu3+9XXFycysrK5Ha7a+Xcpk2bVivjAle7KVOmBLuEy3bNxGuCXQIQkr6Z9U2tjn+x/35X6zFWbSkrK5MkJSQkSJIKCwt1+vRp9enTx+nTqlUrNWnSRAUFBZKkgoICtW3b1gk6kpSRkSG/368dO3ZUeZzy8nL5/f6ABQAA2Klaj7F69ux5wcdVq1atuuQxKysr9dBDD+mWW27RjTfeKEny+XyKiopSfHx8QN+kpCT5fD6nzw+Dztn2s21VycvL404LAAA/E9UKO2fn65x1+vRpbd68Wdu3bz/nC0IvVnZ2trZv364NGzZUa/9LkZubGzCR2u/3KzU1tdaPCwAArrxqhZ1Zs2ZVuX3q1Kk6fvz4JY83fvx4LV68WOvWrVPjxo2d7R6PR6dOndLRo0cD7u6UlJTI4/E4fT7++OOA8c5+Wutsnx9zuVxyuVyXXCcAALj61OicnV/+8peX9L1YxhiNHz9eCxcu1KpVq9S8efOA9k6dOikyMlIrV650tu3evVv79++X1+uVJHm9Xm3btk2lpaVOn+XLl8vtdistLe0yzwgAAFztqv1FoFUpKChQdHT0RffPzs5Wfn6+/va3v6l+/frOHJu4uDjFxMQoLi5Oo0ePVk5OjhISEuR2uzVhwgR5vV5169ZNktSvXz+lpaVp+PDhmjlzpnw+nyZPnqzs7Gzu3gAAgOqFnTvvvDNg3Rij4uJiffrpp3rssccuepw5c+ZIknr06BGwfd68eRo1apSk7x+ZhYeHKysrS+Xl5crIyNBLL73k9I2IiNDixYs1btw4eb1e1atXTyNHjtT06dOrc2oAAMAy1Qo7cXFxAevh4eFq2bKlpk+frn79+l30OBfzip/o6GjNnj1bs2fPPm+fpk2b6v3337/o4wIAgJ+PaoWdefPm1XQdAAAAteKy5uwUFhaqqKhIktSmTRt16NChRooCAACoKdUKO6WlpRo6dKjWrFnjfCT86NGj6tmzp15//XU1atSoJmsEAACotmp99HzChAk6duyYduzYoSNHjujIkSPavn27/H6/HnjggZquEQAAoNqqdWdn6dKlWrFihVq3bu1sS0tL0+zZsy9pgjIAAEBtq9adncrKSkVGRp6zPTIyUpWVlZddFAAAQE2pVtjp1auXHnzwQR08eNDZ9s9//lMTJ05U7969a6w4AACAy1WtsPPf//3f8vv9atasmVq0aKEWLVqoefPm8vv9evHFF2u6RgAAgGqr1pyd1NRU/eMf/9CKFSu0a9cuSVLr1q3Vp0+fGi0OAADgcl3SnZ1Vq1YpLS1Nfr9fYWFh6tu3ryZMmKAJEyaoS5cuatOmjdavX19btQIAAFyySwo7L7zwgsaMGSO3231OW1xcnH7zm9/o+eefr7HiAAAALtclhZ0tW7aof//+523v16+fCgsLL7soAACAmnJJYaekpKTKj5yfVadOHR06dOiyiwIAAKgplxR2/u3f/k3bt28/b/vWrVuVnJx82UUBAADUlEsKOwMHDtRjjz2mkydPntP23XffacqUKbr99ttrrDgAAIDLdUkfPZ88ebLeeecd3XDDDRo/frxatmwpSdq1a5dmz56tiooK/dd//VetFAoAAFAdlxR2kpKStHHjRo0bN065ubkyxkiSwsLClJGRodmzZyspKalWCgUAAKiOS36pYNOmTfX+++/rm2++0Z49e2SM0fXXX69rrrmmNuoDAAC4LNV6g7IkXXPNNerSpUtN1gIAAFDjqvXdWAAAAFcLwg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgtaCGnXXr1mnQoEFKSUlRWFiYFi1aFNA+atQohYWFBSz9+/cP6HPkyBENGzZMbrdb8fHxGj16tI4fP34FzwIAAISyoIadEydOqF27dpo9e/Z5+/Tv31/FxcXO8r//+78B7cOGDdOOHTu0fPlyLV68WOvWrdPYsWNru3QAAHCVqBPMgw8YMEADBgy4YB+XyyWPx1NlW1FRkZYuXapPPvlEnTt3liS9+OKLGjhwoJ599lmlpKTUeM0AAODqEvJzdtasWaPExES1bNlS48aN0+HDh522goICxcfHO0FHkvr06aPw8HBt2rTpvGOWl5fL7/cHLAAAwE4hHXb69++v1157TStXrtTTTz+ttWvXasCAAaqoqJAk+Xw+JSYmBuxTp04dJSQkyOfznXfcvLw8xcXFOUtqamqtngcAAAieoD7G+ilDhw51fm7btq3S09PVokULrVmzRr179672uLm5ucrJyXHW/X4/gQcAAEuF9J2dH7v22mvVsGFD7dmzR5Lk8XhUWloa0OfMmTM6cuTIeef5SN/PA3K73QELAACw01UVdr7++msdPnxYycnJkiSv16ujR4+qsLDQ6bNq1SpVVlaqa9euwSoTAACEkKA+xjp+/Lhzl0aS9u3bp82bNyshIUEJCQmaNm2asrKy5PF4tHfvXj3yyCO67rrrlJGRIUlq3bq1+vfvrzFjxmju3Lk6ffq0xo8fr6FDh/JJLAAAICnId3Y+/fRTdejQQR06dJAk5eTkqEOHDnr88ccVERGhrVu36o477tANN9yg0aNHq1OnTlq/fr1cLpczxoIFC9SqVSv17t1bAwcO1K233qqXX345WKcEAABCTFDv7PTo0UPGmPO2L1u27CfHSEhIUH5+fk2WBQAALHJVzdkBAAC4VIQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFgtqGFn3bp1GjRokFJSUhQWFqZFixYFtBtj9Pjjjys5OVkxMTHq06ePvvjii4A+R44c0bBhw+R2uxUfH6/Ro0fr+PHjV/AsAABAKAtq2Dlx4oTatWun2bNnV9k+c+ZM/fGPf9TcuXO1adMm1atXTxkZGTp58qTTZ9iwYdqxY4eWL1+uxYsXa926dRo7duyVOgUAABDi6gTz4AMGDNCAAQOqbDPG6IUXXtDkyZM1ePBgSdJrr72mpKQkLVq0SEOHDlVRUZGWLl2qTz75RJ07d5Ykvfjiixo4cKCeffZZpaSkXLFzAQAAoSlk5+zs27dPPp9Pffr0cbbFxcWpa9euKigokCQVFBQoPj7eCTqS1KdPH4WHh2vTpk3nHbu8vFx+vz9gAQAAdgrZsOPz+SRJSUlJAduTkpKcNp/Pp8TExID2OnXqKCEhwelTlby8PMXFxTlLampqDVcPAABCRciGndqUm5ursrIyZzlw4ECwSwIAALUkZMOOx+ORJJWUlARsLykpcdo8Ho9KS0sD2s+cOaMjR444faricrnkdrsDFgAAYKeQDTvNmzeXx+PRypUrnW1+v1+bNm2S1+uVJHm9Xh09elSFhYVOn1WrVqmyslJdu3a94jUDAIDQE9RPYx0/flx79uxx1vft26fNmzcrISFBTZo00UMPPaTf//73uv7669W8eXM99thjSklJ0ZAhQyRJrVu3Vv/+/TVmzBjNnTtXp0+f1vjx4zV06FA+iQUAACQFOex8+umn6tmzp7Oek5MjSRo5cqTmz5+vRx55RCdOnNDYsWN19OhR3XrrrVq6dKmio6OdfRYsWKDx48erd+/eCg8PV1ZWlv74xz9e8XMBAAChKahhp0ePHjLGnLc9LCxM06dP1/Tp08/bJyEhQfn5+bVRHgAAsEDIztkBAACoCYQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFgtpMPO1KlTFRYWFrC0atXKaT958qSys7PVoEEDxcbGKisrSyUlJUGsGAAAhJqQDjuS1KZNGxUXFzvLhg0bnLaJEyfqvffe05tvvqm1a9fq4MGDuvPOO4NYLQAACDV1gl3AT6lTp448Hs8528vKyvTnP/9Z+fn56tWrlyRp3rx5at26tT766CN169btSpcKAABCUMjf2fniiy+UkpKia6+9VsOGDdP+/fslSYWFhTp9+rT69Onj9G3VqpWaNGmigoKCC45ZXl4uv98fsAAAADuFdNjp2rWr5s+fr6VLl2rOnDnat2+fbrvtNh07dkw+n09RUVGKj48P2CcpKUk+n++C4+bl5SkuLs5ZUlNTa/EsAABAMIX0Y6wBAwY4P6enp6tr165q2rSp/vrXvyomJqba4+bm5ionJ8dZ9/v9BB4AACwV0nd2fiw+Pl433HCD9uzZI4/Ho1OnTuno0aMBfUpKSqqc4/NDLpdLbrc7YAEAAHa6qsLO8ePHtXfvXiUnJ6tTp06KjIzUypUrnfbdu3dr//798nq9QawSAACEkpB+jPXb3/5WgwYNUtOmTXXw4EFNmTJFERERuvfeexUXF6fRo0crJydHCQkJcrvdmjBhgrxeL5/EAgAAjpAOO19//bXuvfdeHT58WI0aNdKtt96qjz76SI0aNZIkzZo1S+Hh4crKylJ5ebkyMjL00ksvBblqAAAQSkI67Lz++usXbI+Ojtbs2bM1e/bsK1QRAAC42lxVc3YAAAAuFWEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFazJuzMnj1bzZo1U3R0tLp27aqPP/442CUBAIAQYEXYeeONN5STk6MpU6boH//4h9q1a6eMjAyVlpYGuzQAABBkVoSd559/XmPGjNGvfvUrpaWlae7cuapbt65eeeWVYJcGAACCrE6wC7hcp06dUmFhoXJzc51t4eHh6tOnjwoKCqrcp7y8XOXl5c56WVmZJMnv99danSdPnqy1sYGrWW1ed1eKKTfBLgEISbV9fZ8d35gLX4NXfdj517/+pYqKCiUlJQVsT0pK0q5du6rcJy8vT9OmTTtne2pqaq3UCOD8ZsyYEewSANSSuDlxV+Q4x44dU1zc+Y911Yed6sjNzVVOTo6zXllZqSNHjqhBgwYKCwsLYmW4Evx+v1JTU3XgwAG53e5glwOgBnF9/7wYY3Ts2DGlpKRcsN9VH3YaNmyoiIgIlZSUBGwvKSmRx+Opch+XyyWXyxWwLT4+vrZKRIhyu938MgQsxfX983GhOzpnXfUTlKOiotSpUyetXLnS2VZZWamVK1fK6/UGsTIAABAKrvo7O5KUk5OjkSNHqnPnzrrpppv0wgsv6MSJE/rVr34V7NIAAECQWRF27rnnHh06dEiPP/64fD6f2rdvr6VLl54zaRmQvn+MOWXKlHMeZQK4+nF9oyph5qc+rwUAAHAVu+rn7AAAAFwIYQcAAFiNsAMAAKxG2AGqYerUqWrfvn2tH6dZs2Z64YUXav04gG3WrFmjsLAwHT16tFaPM2rUKA0ZMqRWj4HLR9hBjRg1apTCwsLOefX/okWLLvmt1Bf7D/yWLVt0xx13KDExUdHR0WrWrJnuueeeK/Jt97/97W8D3u0EoGqHDh3SuHHj1KRJE7lcLnk8HmVkZOjDDz+s1ePefPPNKi4uvqgXzsF+hB3UmOjoaD399NP65ptvav1Yhw4dUu/evZWQkKBly5apqKhI8+bNU0pKik6cOFHtcU+dOnVR/WJjY9WgQYNqHwf4ucjKytJnn32mV199VZ9//rneffdd9ejRQ4cPH67WeMYYnTlz5if7RUVFyePx8BVAkETYQQ3q06ePPB6P8vLyLtjv7bffVps2beRyudSsWTM999xzTluPHj30f//3f5o4caLCwsLO+4vqww8/VFlZmf70pz+pQ4cOat68uXr27KlZs2apefPmkqT58+ef8zUgP77TdPZx1J/+9Cc1b95c0dHRevnll5WSkqLKysqAfQcPHqxf//rXAftJ0gcffKDo6Ohzbpc/+OCD6tWrl7O+YcMG3XbbbYqJiVFqaqoeeOCBgGBWWlqqQYMGKSYmRs2bN9eCBQsu+OcIhLqjR49q/fr1evrpp9WzZ081bdpUN910k3Jzc3XHHXfoq6++UlhYmDZv3hywT1hYmNasWSPp/z+OWrJkiTp16iSXy6VXXnlFYWFh53zZ86xZs9SiRYuA/Y4ePSq/36+YmBgtWbIkoP/ChQtVv359ffvtt5KkAwcO6O6771Z8fLwSEhI0ePBgffXVV07/iooK5eTkKD4+Xg0aNNAjjzzyk9+2jdBA2EGNiYiI0FNPPaUXX3xRX3/9dZV9CgsLdffdd2vo0KHatm2bpk6dqscee0zz58+XJL3zzjtq3Lixpk+fruLiYhUXF1c5jsfj0ZkzZ7Rw4cLL/mWzZ88evf3223rnnXe0efNm/eIXv9Dhw4e1evVqp8+RI0e0dOlSDRs27Jz9e/furfj4eL399tvOtoqKCr3xxhtO/71796p///7KysrS1q1b9cYbb2jDhg0aP368s8+oUaN04MABrV69Wm+99ZZeeumlK/JIDqgtsbGxio2N1aJFi1ReXn5ZY/3ud7/TjBkzVFRUpLvuukudO3c+5z8ECxYs0H333XfOvm63W7fffrvy8/PP6T9kyBDVrVtXp0+fVkZGhurXr6/169frww8/VGxsrPr37+/c8X3uuec0f/58vfLKK9qwYYOOHDmihQsXXtZ54QoxQA0YOXKkGTx4sDHGmG7duplf//rXxhhjFi5caH741+y+++4zffv2Ddj34YcfNmlpac5606ZNzaxZs37ymI8++qipU6eOSUhIMP379zczZ840Pp/PaZ83b56Ji4sL2OfH9UyZMsVERkaa0tLSgH6DBw92zsEYY/7nf/7HpKSkmIqKCme/du3aOe0PPvig6dWrl7O+bNky43K5zDfffGOMMWb06NFm7NixAcdYv369CQ8PN999953ZvXu3kWQ+/vhjp72oqMhIuqg/CyBUvfXWW+aaa64x0dHR5uabbza5ublmy5Ytxhhj9u3bZySZzz77zOn/zTffGElm9erVxhhjVq9ebSSZRYsWBYw7a9Ys06JFC2f97DVUVFQUsN/Za3DhwoUmNjbWnDhxwhhjTFlZmYmOjjZLliwxxhjzl7/8xbRs2dJUVlY6Y5aXl5uYmBizbNkyY4wxycnJZubMmU776dOnTePGjZ3ffQhd3NlBjXv66af16quvqqio6Jy2oqIi3XLLLQHbbrnlFn3xxReqqKi4pOM8+eST8vl8mjt3rtq0aaO5c+eqVatW2rZt2yWN07RpUzVq1Chg27Bhw/T22287/xtdsGCBhg4dqvDwqi+ZYcOGac2aNTp48KDTPzMz03mMtmXLFs2fP9/5n25sbKwyMjJUWVmpffv2qaioSHXq1FGnTp2cMVu1anXOYzjgapOVlaWDBw/q3XffVf/+/bVmzRp17NjRuZt7sTp37hywPnToUH311Vf66KOPJH1/zXXs2FGtWrWqcv+BAwcqMjJS7777rqTvH6e73W716dNH0vfX6J49e1S/fn3nGk1ISNDJkye1d+9elZWVqbi4WF27dnXGrFOnzjl1ITQRdlDjunfvroyMDOXm5tb6sRo0aKBf/OIXevbZZ1VUVKSUlBQ9++yzkqTw8PBzHnGdPn36nDHq1at3zrZBgwbJGKO///3vOnDggNavX1/lI6yzunTpohYtWuj111/Xd999p4ULFwb0P378uH7zm99o8+bNzrJlyxZ98cUXzhwDwFbR0dHq27evHnvsMW3cuFGjRo3SlClTnP88/PA6reoalc69Tj0ej3r16uU8msrPz7/gNRoVFaW77roroP8999yjOnW+/4rI48ePq1OnTgHX6ObNm/X5559X+WgMVxcrvggUoWfGjBlq3769WrZsGbC9devW53zk9MMPP9QNN9ygiIgISd//UrrUuzxn92vRooUz6bdRo0Y6duyYTpw44fyi/OFEyAuJjo7WnXfeqQULFmjPnj1q2bKlOnbseMF9hg0bpgULFqhx48YKDw9XZmam09axY0ft3LlT1113XZX7tmrVSmfOnFFhYaG6dOkiSdq9e3etvyMECIa0tDQtWrTIuaNaXFysDh06SLr4a1T6/pp75JFHdO+99+rLL7/U0KFDf7J/3759tWPHDq1atUq///3vnbaOHTvqjTfeUGJiotxud5X7Jycna9OmTerevbskOdfsT/1uQAgI8mM0WOKHc3bOGj58uImOjg6YI1NYWGjCw8PN9OnTze7du838+fNNTEyMmTdvntOnb9++5o477jBff/21OXToUJXHe++998ywYcPMe++9Z3bv3m127dplnnnmGRMREWFee+01Y4wxhw8fNvXq1TMPPPCA2bNnj1mwYIFJSUk5Z87OD+fe/NDy5cuNy+UyLVu2NE888URAW1X7ffHFF0aSSU9PN6NHjw5o27Jli4mJiTHZ2dnms88+M59//rlZtGiRyc7Odvr079/fdOjQwXz00Ufm008/NbfeequJiYlhzg6uWv/6179Mz549zV/+8hezZcsW8+WXX5q//vWvJikpyZkT161bN3PbbbeZnTt3mjVr1pibbrqpyjk7Z+fe/JDf7zcxMTGmXbt2pnfv3gFtVe1XWVlpUlNTTbt27QLm+xhjzIkTJ8z1119vevToYdatW2e+/PJLs3r1ajNhwgRz4MABY4wxM2bMMAkJCWbhwoWmqKjIjBkzxtSvX585O1cBwg5qRFVhZ9++fSYqKsr8OFO/9dZbJi0tzURGRpomTZqYZ555JqC9oKDApKenG5fLdc6+Z+3du9eMGTPG3HDDDSYmJsbEx8ebLl26BIQmY76flHjdddeZmJgYc/vtt5uXX375osNORUWFSU5ONpLM3r17A9rOt9/ZX9SrVq06p+3jjz82ffv2NbGxsaZevXomPT3dPPnkk057cXGxyczMNC6XyzRp0sS89tprFz1ZGwhFJ0+eNL/73e9Mx44dTVxcnKlbt65p2bKlmTx5svn222+NMcbs3LnTeL1eExMTY9q3b28++OCDiw47xhhz9913G0nmlVdeCdh+vv0eeeQRI8k8/vjj54xVXFxsRowYYRo2bGhcLpe59tprzZgxY0xZWZkx5vsJyQ8++KBxu90mPj7e5OTkmBEjRhB2rgJhxvCSAAAAYC8mKAMAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgtf8HeSa2vUXuPg8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.value_counts()\n",
    "x_label = ['Not Survived', 'Survived']\n",
    "sns.barplot(x=x_label, y=y_train.value_counts(), palette=['grey', 'green'])\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17e6cc6f",
   "metadata": {
    "papermill": {
     "duration": 0.02968,
     "end_time": "2024-08-03T04:39:21.025190",
     "exception": false,
     "start_time": "2024-08-03T04:39:20.995510",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- The dataset would not be considered to imbalance dataset. \n",
    "- Accuracy can be used to evaluate model during comparision process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc822cf6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:21.089607Z",
     "iopub.status.busy": "2024-08-03T04:39:21.089191Z",
     "iopub.status.idle": "2024-08-03T04:39:21.097337Z",
     "shell.execute_reply": "2024-08-03T04:39:21.096098Z"
    },
    "papermill": {
     "duration": 0.041908,
     "end_time": "2024-08-03T04:39:21.099714",
     "exception": false,
     "start_time": "2024-08-03T04:39:21.057806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio of died to survived =  1.6846846846846846\n"
     ]
    }
   ],
   "source": [
    "print('ratio of died to survived = ', y_train.value_counts()[0]/y_train.value_counts()[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da4ad40",
   "metadata": {
    "papermill": {
     "duration": 0.029988,
     "end_time": "2024-08-03T04:39:21.160290",
     "exception": false,
     "start_time": "2024-08-03T04:39:21.130302",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Round 2 - Improving Performance - Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef9259c",
   "metadata": {
    "papermill": {
     "duration": 0.030646,
     "end_time": "2024-08-03T04:39:21.222079",
     "exception": false,
     "start_time": "2024-08-03T04:39:21.191433",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Seeking insight about survivorship - Data Visualization / Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f2a3fc4",
   "metadata": {
    "papermill": {
     "duration": 0.030029,
     "end_time": "2024-08-03T04:39:21.282711",
     "exception": false,
     "start_time": "2024-08-03T04:39:21.252682",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Age-Survivorship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "40a39073",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:21.345717Z",
     "iopub.status.busy": "2024-08-03T04:39:21.345330Z",
     "iopub.status.idle": "2024-08-03T04:39:21.758473Z",
     "shell.execute_reply": "2024-08-03T04:39:21.757116Z"
    },
    "papermill": {
     "duration": 0.447045,
     "end_time": "2024-08-03T04:39:21.760973",
     "exception": false,
     "start_time": "2024-08-03T04:39:21.313928",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/2183837531.py:9: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  age_survivor_cut = age_survivor.groupby(pd.cut(age_survivor['Age'], bins=10)).sum().drop(columns='Age')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_survivor = train_onehot[['Age', 'Survived']]\n",
    "survived_dummy = pd.get_dummies(age_survivor['Survived']).astype(int)\n",
    "age_survivor = pd.DataFrame({'Not Survived': survived_dummy[0], 'Survived': survived_dummy[1], 'Age' : age_survivor['Age']})\n",
    "#age_survivor\n",
    "\n",
    "#age_survivor_cut = age_survivor.groupby(pd.cut(age_survivor['Age'], labels=['Child', 'Adolescence', 'Lower-Adult', 'Middle-Adult', 'Elderly'], bins=[0, 13, 18, 35, 60, np.inf])).sum().drop(columns='Age')\n",
    "#age_survivor_cut\n",
    "\n",
    "age_survivor_cut = age_survivor.groupby(pd.cut(age_survivor['Age'], bins=10)).sum().drop(columns='Age')\n",
    "\n",
    "age_survivor_cut.plot(kind='bar', figsize=(10,5), color=['Grey', 'Green'], rot=90)\n",
    "plt.title('Age-Survivorship Distribution')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c2dc021",
   "metadata": {
    "papermill": {
     "duration": 0.030867,
     "end_time": "2024-08-03T04:39:21.823710",
     "exception": false,
     "start_time": "2024-08-03T04:39:21.792843",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Number of children who survived more than died.\n",
    "- In contrast, in other age intervals, more people died than survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "74ae5050",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:21.887405Z",
     "iopub.status.busy": "2024-08-03T04:39:21.887025Z",
     "iopub.status.idle": "2024-08-03T04:39:21.899533Z",
     "shell.execute_reply": "2024-08-03T04:39:21.898491Z"
    },
    "papermill": {
     "duration": 0.047083,
     "end_time": "2024-08-03T04:39:21.901955",
     "exception": false,
     "start_time": "2024-08-03T04:39:21.854872",
     "status": "completed"
    },
    "tags": []
   },
   "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>Not Survived</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0.34, 8.378]</th>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8.378, 16.336]</th>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(16.336, 24.294]</th>\n",
       "      <td>114</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(24.294, 32.252]</th>\n",
       "      <td>229</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(32.252, 40.21]</th>\n",
       "      <td>66</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40.21, 48.168]</th>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(48.168, 56.126]</th>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(56.126, 64.084]</th>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(64.084, 72.042]</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(72.042, 80.0]</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Not Survived  Survived\n",
       "Age                                     \n",
       "(0.34, 8.378]               18        36\n",
       "(8.378, 16.336]             27        19\n",
       "(16.336, 24.294]           114        63\n",
       "(24.294, 32.252]           229       117\n",
       "(32.252, 40.21]             66        52\n",
       "(40.21, 48.168]             46        24\n",
       "(48.168, 56.126]            24        21\n",
       "(56.126, 64.084]            15         9\n",
       "(64.084, 72.042]             9         0\n",
       "(72.042, 80.0]               1         1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_survivor_cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "19130711",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:21.967308Z",
     "iopub.status.busy": "2024-08-03T04:39:21.966515Z",
     "iopub.status.idle": "2024-08-03T04:39:21.972786Z",
     "shell.execute_reply": "2024-08-03T04:39:21.971586Z"
    },
    "papermill": {
     "duration": 0.041386,
     "end_time": "2024-08-03T04:39:21.975261",
     "exception": false,
     "start_time": "2024-08-03T04:39:21.933875",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# inspecting conditional probabiliry (survival rate if knowing age)\n",
    "survival_rate_byage = age_survivor_cut['Survived']/(age_survivor_cut['Survived'] + age_survivor_cut['Not Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "83669bbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:22.039746Z",
     "iopub.status.busy": "2024-08-03T04:39:22.039342Z",
     "iopub.status.idle": "2024-08-03T04:39:22.049401Z",
     "shell.execute_reply": "2024-08-03T04:39:22.048383Z"
    },
    "papermill": {
     "duration": 0.045111,
     "end_time": "2024-08-03T04:39:22.051784",
     "exception": false,
     "start_time": "2024-08-03T04:39:22.006673",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "(0.34, 8.378]       0.666667\n",
       "(8.378, 16.336]     0.413043\n",
       "(16.336, 24.294]    0.355932\n",
       "(24.294, 32.252]    0.338150\n",
       "(32.252, 40.21]     0.440678\n",
       "(40.21, 48.168]     0.342857\n",
       "(48.168, 56.126]    0.466667\n",
       "(56.126, 64.084]    0.375000\n",
       "(64.084, 72.042]    0.000000\n",
       "(72.042, 80.0]      0.500000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_rate_byage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7271fe5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:22.117584Z",
     "iopub.status.busy": "2024-08-03T04:39:22.117198Z",
     "iopub.status.idle": "2024-08-03T04:39:22.423745Z",
     "shell.execute_reply": "2024-08-03T04:39:22.422482Z"
    },
    "papermill": {
     "duration": 0.342089,
     "end_time": "2024-08-03T04:39:22.426271",
     "exception": false,
     "start_time": "2024-08-03T04:39:22.084182",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Survival Rate by Age')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_rate_byage.plot(kind='bar')\n",
    "plt.title('Survival Rate by Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd9127d",
   "metadata": {
    "papermill": {
     "duration": 0.031399,
     "end_time": "2024-08-03T04:39:22.489935",
     "exception": false,
     "start_time": "2024-08-03T04:39:22.458536",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Difficult to observe pattern\n",
    "\n",
    "- Re-define bins to seek pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5d19f63f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:22.556665Z",
     "iopub.status.busy": "2024-08-03T04:39:22.555821Z",
     "iopub.status.idle": "2024-08-03T04:39:22.871074Z",
     "shell.execute_reply": "2024-08-03T04:39:22.869810Z"
    },
    "papermill": {
     "duration": 0.351368,
     "end_time": "2024-08-03T04:39:22.873704",
     "exception": false,
     "start_time": "2024-08-03T04:39:22.522336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/3850789614.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  age_survivor_cut = age_survivor.groupby(pd.cut(age_survivor['Age'], bins=[0, 8, 25,40,np.inf])).sum().drop(columns='Age')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_survivor_cut = age_survivor.groupby(pd.cut(age_survivor['Age'], bins=[0, 8, 25,40,np.inf])).sum().drop(columns='Age')\n",
    "\n",
    "age_survivor_cut.plot(kind='bar', figsize=(10,5), color=['Grey', 'Green'], rot=0)\n",
    "plt.title('Age-Survivorship Distribution')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0ef7b512",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:22.943781Z",
     "iopub.status.busy": "2024-08-03T04:39:22.943341Z",
     "iopub.status.idle": "2024-08-03T04:39:22.955321Z",
     "shell.execute_reply": "2024-08-03T04:39:22.953884Z"
    },
    "papermill": {
     "duration": 0.050512,
     "end_time": "2024-08-03T04:39:22.957785",
     "exception": false,
     "start_time": "2024-08-03T04:39:22.907273",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "(0.0, 8.0]      0.666667\n",
       "(8.0, 25.0]     0.356275\n",
       "(25.0, 40.0]    0.370455\n",
       "(40.0, inf]     0.366667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_rate_byage = age_survivor_cut['Survived']/(age_survivor_cut['Survived'] + age_survivor_cut['Not Survived'])\n",
    "survival_rate_byage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1bfba530",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:23.026784Z",
     "iopub.status.busy": "2024-08-03T04:39:23.026369Z",
     "iopub.status.idle": "2024-08-03T04:39:23.273231Z",
     "shell.execute_reply": "2024-08-03T04:39:23.271983Z"
    },
    "papermill": {
     "duration": 0.284306,
     "end_time": "2024-08-03T04:39:23.275678",
     "exception": false,
     "start_time": "2024-08-03T04:39:22.991372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Survival Rate by Age')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_rate_byage.plot(kind='bar')\n",
    "plt.title('Survival Rate by Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979aff4b",
   "metadata": {
    "papermill": {
     "duration": 0.033129,
     "end_time": "2024-08-03T04:39:23.342026",
     "exception": false,
     "start_time": "2024-08-03T04:39:23.308897",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Different in survival rate can be divided into 2 groups : age <=8 , age > 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "975c5ac0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:23.411231Z",
     "iopub.status.busy": "2024-08-03T04:39:23.410788Z",
     "iopub.status.idle": "2024-08-03T04:39:23.418441Z",
     "shell.execute_reply": "2024-08-03T04:39:23.416645Z"
    },
    "papermill": {
     "duration": 0.049317,
     "end_time": "2024-08-03T04:39:23.424597",
     "exception": false,
     "start_time": "2024-08-03T04:39:23.375280",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# feature engineering on 'age' feature\n",
    "# grouping age<=8 label to '1', age>8 to '0'\n",
    "train_onehot['Age_8'] = train_onehot['Age']<=8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a57b3138",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:23.492848Z",
     "iopub.status.busy": "2024-08-03T04:39:23.492452Z",
     "iopub.status.idle": "2024-08-03T04:39:23.499803Z",
     "shell.execute_reply": "2024-08-03T04:39:23.498720Z"
    },
    "papermill": {
     "duration": 0.044306,
     "end_time": "2024-08-03T04:39:23.502079",
     "exception": false,
     "start_time": "2024-08-03T04:39:23.457773",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot['Age_8'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3538c15c",
   "metadata": {
    "papermill": {
     "duration": 0.033398,
     "end_time": "2024-08-03T04:39:23.570201",
     "exception": false,
     "start_time": "2024-08-03T04:39:23.536803",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Sex-Survivorship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "65e3e56e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:23.639685Z",
     "iopub.status.busy": "2024-08-03T04:39:23.638467Z",
     "iopub.status.idle": "2024-08-03T04:39:23.914578Z",
     "shell.execute_reply": "2024-08-03T04:39:23.913083Z"
    },
    "papermill": {
     "duration": 0.313551,
     "end_time": "2024-08-03T04:39:23.917194",
     "exception": false,
     "start_time": "2024-08-03T04:39:23.603643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sex-Survivorship')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# relationship between survived and sex\n",
    "#train_onehot.head(5)\n",
    "not_survived = -train_onehot['Survived'].astype(bool).rename('Not Survived')\n",
    "sex_survivorship = pd.concat([train_onehot[['Survived' ,'female', 'male']], not_survived], axis=1)\n",
    "#sex_survived\n",
    "sex_survivorship = sex_survivorship.groupby(['female', 'male']).sum()\n",
    "sex_survivorship = sex_survivorship.reset_index().drop(columns=['female', 'male'])\n",
    "sex_survivorship.insert(0, 'Sex', ['Male', 'Female'])\n",
    "sex_survivorship.set_index('Sex', inplace=True)\n",
    "\n",
    "sex_survivorship.plot(kind='bar', rot=0, color=['Green', 'Grey'])\n",
    "plt.ylabel('Count')\n",
    "plt.title('Sex-Survivorship')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f1f172",
   "metadata": {
    "papermill": {
     "duration": 0.035739,
     "end_time": "2024-08-03T04:39:23.987503",
     "exception": false,
     "start_time": "2024-08-03T04:39:23.951764",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Many of survivors are 'female'.\n",
    "- Many of deaths are 'male'.\n",
    "- On the titanic, males are the greatest portion of the passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7c80121b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:24.057185Z",
     "iopub.status.busy": "2024-08-03T04:39:24.056762Z",
     "iopub.status.idle": "2024-08-03T04:39:24.067527Z",
     "shell.execute_reply": "2024-08-03T04:39:24.066262Z"
    },
    "papermill": {
     "duration": 0.048376,
     "end_time": "2024-08-03T04:39:24.070025",
     "exception": false,
     "start_time": "2024-08-03T04:39:24.021649",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Not Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>109</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>233</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived  Not Survived\n",
       "Sex                           \n",
       "Male         109           468\n",
       "Female       233            81"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_survivorship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6d0ef129",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:24.141353Z",
     "iopub.status.busy": "2024-08-03T04:39:24.140977Z",
     "iopub.status.idle": "2024-08-03T04:39:24.151654Z",
     "shell.execute_reply": "2024-08-03T04:39:24.150318Z"
    },
    "papermill": {
     "duration": 0.048688,
     "end_time": "2024-08-03T04:39:24.153864",
     "exception": false,
     "start_time": "2024-08-03T04:39:24.105176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival Rate\n",
      "P(Survived | Male) =  0.1889\n",
      "P(Survived | Female) =  0.742\n",
      "P(Survived and Male) =  0.1223\n",
      "P(Survived and Female) =  0.2615\n"
     ]
    }
   ],
   "source": [
    "total_males = sex_survivorship[sex_survivorship.index == 'Male'].values.sum()\n",
    "total_females = sex_survivorship[sex_survivorship.index == 'Female'].values.sum()\n",
    "\n",
    "survived_males = sex_survivorship[sex_survivorship.index == 'Male']['Survived'].values\n",
    "survived_females = sex_survivorship[sex_survivorship.index == 'Female']['Survived'].values\n",
    "\n",
    "total_passengers = total_males+total_females # equals to total of survived and total of non-survived\n",
    "survived_given_males_prob = survived_males/total_males # P(Survived|Male)\n",
    "survived_given_females_prob = survived_females/total_females # P(Survived|Female)\n",
    "survived_and_males_prob = survived_males/total_passengers\n",
    "survived_and_females_prob = survived_females/total_passengers\n",
    "\n",
    "print('Survival Rate')\n",
    "print('P(Survived | Male) = ', round(survived_given_males_prob[0],4)) \n",
    "print('P(Survived | Female) = ', round(survived_given_females_prob[0], 4))\n",
    "print('P(Survived and Male) = ', round(survived_and_males_prob[0], 4))\n",
    "print('P(Survived and Female) = ', round(survived_and_females_prob[0], 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7be41a",
   "metadata": {
    "papermill": {
     "duration": 0.033347,
     "end_time": "2024-08-03T04:39:24.221398",
     "exception": false,
     "start_time": "2024-08-03T04:39:24.188051",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- The chance of survival for a male compared to other males is approximately 18.9%. [**]\n",
    "- The chance of survival for a female compared to other females is approximately 74.3%. [**]\n",
    "- The chance of survival for a male compared to all passengers is approximately 12.2%.\n",
    "- The chance of survival for a female compared to all passengers is approximately 26.1%.\n",
    "\n",
    "[**] **Conclusion: Knowing a passenger’s gender provides significant information about their likelihood of survival, as there is a marked difference in survival rates between males and females.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "741f2d38",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:24.291319Z",
     "iopub.status.busy": "2024-08-03T04:39:24.290563Z",
     "iopub.status.idle": "2024-08-03T04:39:24.295578Z",
     "shell.execute_reply": "2024-08-03T04:39:24.294448Z"
    },
    "papermill": {
     "duration": 0.042669,
     "end_time": "2024-08-03T04:39:24.297928",
     "exception": false,
     "start_time": "2024-08-03T04:39:24.255259",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# P(Survived|Male), P(Survived|Female) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f4390104",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:24.369890Z",
     "iopub.status.busy": "2024-08-03T04:39:24.369483Z",
     "iopub.status.idle": "2024-08-03T04:39:24.602056Z",
     "shell.execute_reply": "2024-08-03T04:39:24.600974Z"
    },
    "papermill": {
     "duration": 0.271438,
     "end_time": "2024-08-03T04:39:24.604469",
     "exception": false,
     "start_time": "2024-08-03T04:39:24.333031",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Sex'].value_counts().plot(kind='bar', rot=0, color=['darkblue', 'brown'])\n",
    "plt.title('Sex Distribution')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72427257",
   "metadata": {
    "papermill": {
     "duration": 0.033782,
     "end_time": "2024-08-03T04:39:24.672678",
     "exception": false,
     "start_time": "2024-08-03T04:39:24.638896",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### P Class-Survivorship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9c3e778b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:24.744405Z",
     "iopub.status.busy": "2024-08-03T04:39:24.743995Z",
     "iopub.status.idle": "2024-08-03T04:39:24.751383Z",
     "shell.execute_reply": "2024-08-03T04:39:24.750316Z"
    },
    "papermill": {
     "duration": 0.046893,
     "end_time": "2024-08-03T04:39:24.753665",
     "exception": false,
     "start_time": "2024-08-03T04:39:24.706772",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pclass_survived = train_onehot[['Survived', 'Pclass']]\n",
    "pclass_survived.insert(1,'Not Survived' ,pd.Series(not_survived.values)) # not_survived variable reuses from sex-survivorship analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5a3f5fcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:24.826597Z",
     "iopub.status.busy": "2024-08-03T04:39:24.825548Z",
     "iopub.status.idle": "2024-08-03T04:39:25.092238Z",
     "shell.execute_reply": "2024-08-03T04:39:25.091020Z"
    },
    "papermill": {
     "duration": 0.3059,
     "end_time": "2024-08-03T04:39:25.094627",
     "exception": false,
     "start_time": "2024-08-03T04:39:24.788727",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'P Class-Survivorship')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass_survived = pclass_survived.groupby('Pclass').sum()\n",
    "pclass_survived.plot(kind='bar', rot=0, color=['Green', 'Grey'])\n",
    "plt.title('P Class-Survivorship')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "667a79e3",
   "metadata": {
    "papermill": {
     "duration": 0.034594,
     "end_time": "2024-08-03T04:39:25.163954",
     "exception": false,
     "start_time": "2024-08-03T04:39:25.129360",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- For class-1 passengers, the number of survivors was greater than the number of those who did not survive.\n",
    "- For class-2 passengers, the number of surviors and non-survivors were nearly the same.\n",
    "- For class-3 passengers, the number of non-survivors were very larger than the number of non-survivors.\n",
    "- On the titanic, class-3 passengers are the largest group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "03c394e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:25.234865Z",
     "iopub.status.busy": "2024-08-03T04:39:25.234456Z",
     "iopub.status.idle": "2024-08-03T04:39:25.244660Z",
     "shell.execute_reply": "2024-08-03T04:39:25.243602Z"
    },
    "papermill": {
     "duration": 0.048632,
     "end_time": "2024-08-03T04:39:25.247185",
     "exception": false,
     "start_time": "2024-08-03T04:39:25.198553",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Not Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>136</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>119</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived  Not Survived\n",
       "Pclass                        \n",
       "1            136            80\n",
       "2             87            97\n",
       "3            119           372"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass_survived\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "59981bd0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:25.320264Z",
     "iopub.status.busy": "2024-08-03T04:39:25.319129Z",
     "iopub.status.idle": "2024-08-03T04:39:25.326101Z",
     "shell.execute_reply": "2024-08-03T04:39:25.324993Z"
    },
    "papermill": {
     "duration": 0.045919,
     "end_time": "2024-08-03T04:39:25.328506",
     "exception": false,
     "start_time": "2024-08-03T04:39:25.282587",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Survived | Class1) =  0.6296\n",
      "P(Survived | Class2) =  0.4728\n",
      "P(Survived | Class3) =  0.2424\n"
     ]
    }
   ],
   "source": [
    "survived_given_class1_prob = round(136/(136+80),4)\n",
    "survived_given_class2_prob = round(87/(87+97),4)\n",
    "survived_given_class3_prob = round(119/(119+372),4)\n",
    "\n",
    "print('P(Survived | Class1) = ', survived_given_class1_prob)\n",
    "print('P(Survived | Class2) = ', survived_given_class2_prob)\n",
    "print('P(Survived | Class3) = ', survived_given_class3_prob)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b458e9a",
   "metadata": {
    "papermill": {
     "duration": 0.034639,
     "end_time": "2024-08-03T04:39:25.398283",
     "exception": false,
     "start_time": "2024-08-03T04:39:25.363644",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Different between survival rate at each class are remarkable.\n",
    "- Class information would be significant for training model.\n",
    "- Re ordinal class 1 to 1, class 2 to 0, class 3 to -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "71256f6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:25.472478Z",
     "iopub.status.busy": "2024-08-03T04:39:25.472079Z",
     "iopub.status.idle": "2024-08-03T04:39:25.490064Z",
     "shell.execute_reply": "2024-08-03T04:39:25.488873Z"
    },
    "papermill": {
     "duration": 0.057577,
     "end_time": "2024-08-03T04:39:25.492671",
     "exception": false,
     "start_time": "2024-08-03T04:39:25.435094",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def class_reassign(x):\n",
    "    if x['Pclass'] == 2 : return 0\n",
    "    elif x['Pclass'] == 3 : return -1\n",
    "    else : return 1\n",
    "train_onehot['Pclass'] = train_onehot.apply(class_reassign, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dbcbe745",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:25.564861Z",
     "iopub.status.busy": "2024-08-03T04:39:25.564494Z",
     "iopub.status.idle": "2024-08-03T04:39:25.584330Z",
     "shell.execute_reply": "2024-08-03T04:39:25.583250Z"
    },
    "papermill": {
     "duration": 0.058254,
     "end_time": "2024-08-03T04:39:25.586643",
     "exception": false,
     "start_time": "2024-08-03T04:39:25.528389",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>Age_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age  SibSp  Parch     Fare  female   male      C  \\\n",
       "0           0      -1  22.0      1      0   7.2500   False   True  False   \n",
       "1           1       1  38.0      1      0  71.2833    True  False   True   \n",
       "2           1      -1  26.0      0      0   7.9250    True  False  False   \n",
       "3           1       1  35.0      1      0  53.1000    True  False  False   \n",
       "4           0      -1  35.0      0      0   8.0500   False   True  False   \n",
       "..        ...     ...   ...    ...    ...      ...     ...    ...    ...   \n",
       "886         0       0  27.0      0      0  13.0000   False   True  False   \n",
       "887         1       1  19.0      0      0  30.0000    True  False  False   \n",
       "888         0      -1  28.0      1      2  23.4500    True  False  False   \n",
       "889         1       1  26.0      0      0  30.0000   False   True   True   \n",
       "890         0      -1  32.0      0      0   7.7500   False   True  False   \n",
       "\n",
       "         Q      S  Age_8  \n",
       "0    False   True  False  \n",
       "1    False  False  False  \n",
       "2    False   True  False  \n",
       "3    False   True  False  \n",
       "4    False   True  False  \n",
       "..     ...    ...    ...  \n",
       "886  False   True  False  \n",
       "887  False   True  False  \n",
       "888  False   True  False  \n",
       "889  False  False  False  \n",
       "890   True  False  False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e7ad94",
   "metadata": {
    "papermill": {
     "duration": 0.035262,
     "end_time": "2024-08-03T04:39:25.657321",
     "exception": false,
     "start_time": "2024-08-03T04:39:25.622059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Embarked-Survivorship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0c801dcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:25.729390Z",
     "iopub.status.busy": "2024-08-03T04:39:25.728413Z",
     "iopub.status.idle": "2024-08-03T04:39:26.047216Z",
     "shell.execute_reply": "2024-08-03T04:39:26.046004Z"
    },
    "papermill": {
     "duration": 0.357389,
     "end_time": "2024-08-03T04:39:26.049611",
     "exception": false,
     "start_time": "2024-08-03T04:39:25.692222",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "embarked_survived = train[['Survived', 'Embarked']] # uses the original dataset instead of hot-encoded dataset\n",
    "embarked_survived.insert(1,'Not Survived' ,not_survived.values)\n",
    "embarked_survived = embarked_survived.groupby('Embarked').sum()\n",
    "embarked_survived.plot(kind='bar', rot=0, color=['Green', 'Grey'])\n",
    "plt.title('Embarked-Survivorship')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec35b7c",
   "metadata": {
    "papermill": {
     "duration": 0.035993,
     "end_time": "2024-08-03T04:39:26.122821",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.086828",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Smallest of passengers are from 'Q\" and largest of passengers are from 'S'\n",
    "- Passengers from 'Q' and 'S' have proability to survived nearly the same. (Need precisely checking by calculation.\n",
    "- Passengers from 'C' have differentiated in survived proability when compared to 'Q' and 'S'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7304f01e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:26.196081Z",
     "iopub.status.busy": "2024-08-03T04:39:26.195657Z",
     "iopub.status.idle": "2024-08-03T04:39:26.205279Z",
     "shell.execute_reply": "2024-08-03T04:39:26.204238Z"
    },
    "papermill": {
     "duration": 0.048745,
     "end_time": "2024-08-03T04:39:26.207506",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.158761",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Not Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>93</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>30</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>217</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived  Not Survived\n",
       "Embarked                        \n",
       "C               93            75\n",
       "Q               30            47\n",
       "S              217           427"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "678d5001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:26.283297Z",
     "iopub.status.busy": "2024-08-03T04:39:26.282838Z",
     "iopub.status.idle": "2024-08-03T04:39:26.290020Z",
     "shell.execute_reply": "2024-08-03T04:39:26.288740Z"
    },
    "papermill": {
     "duration": 0.047839,
     "end_time": "2024-08-03T04:39:26.292415",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.244576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Survived | C) =  0.5536\n",
      "P(Survived | Q) =  0.3896\n",
      "P(Survived | S) =  0.337\n"
     ]
    }
   ],
   "source": [
    "survived_given_C = round(93/(93+75),4)\n",
    "survived_given_Q = round(30/(30+47),4)\n",
    "survived_given_S = round(217/(217+427),4)\n",
    "\n",
    "print('P(Survived | C) = ', survived_given_C)\n",
    "print('P(Survived | Q) = ', survived_given_Q)\n",
    "print('P(Survived | S) = ', survived_given_S)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c3ccb5",
   "metadata": {
    "papermill": {
     "duration": 0.042895,
     "end_time": "2024-08-03T04:39:26.373112",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.330217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Survival rate if we know class 'Q' and 'S' are a little bit different. \n",
    "- But there are remarkable survival rate when we know class 'C'\n",
    "- **From the insight above, we will do feature engineering that we will create new feature for checking whether passenger's class is 'C' or not 'C' to make the model simpler.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fe520eff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:26.448938Z",
     "iopub.status.busy": "2024-08-03T04:39:26.448501Z",
     "iopub.status.idle": "2024-08-03T04:39:26.454843Z",
     "shell.execute_reply": "2024-08-03T04:39:26.453754Z"
    },
    "papermill": {
     "duration": 0.04775,
     "end_time": "2024-08-03T04:39:26.457201",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.409451",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_onehot['embarked_C'] = (train['Embarked'] == 'C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3e2a575e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:26.538239Z",
     "iopub.status.busy": "2024-08-03T04:39:26.536956Z",
     "iopub.status.idle": "2024-08-03T04:39:26.563172Z",
     "shell.execute_reply": "2024-08-03T04:39:26.561986Z"
    },
    "papermill": {
     "duration": 0.07132,
     "end_time": "2024-08-03T04:39:26.565594",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.494274",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>Age_8</th>\n",
       "      <th>embarked_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age  SibSp  Parch     Fare  female   male      C  \\\n",
       "0           0      -1  22.0      1      0   7.2500   False   True  False   \n",
       "1           1       1  38.0      1      0  71.2833    True  False   True   \n",
       "2           1      -1  26.0      0      0   7.9250    True  False  False   \n",
       "3           1       1  35.0      1      0  53.1000    True  False  False   \n",
       "4           0      -1  35.0      0      0   8.0500   False   True  False   \n",
       "..        ...     ...   ...    ...    ...      ...     ...    ...    ...   \n",
       "886         0       0  27.0      0      0  13.0000   False   True  False   \n",
       "887         1       1  19.0      0      0  30.0000    True  False  False   \n",
       "888         0      -1  28.0      1      2  23.4500    True  False  False   \n",
       "889         1       1  26.0      0      0  30.0000   False   True   True   \n",
       "890         0      -1  32.0      0      0   7.7500   False   True  False   \n",
       "\n",
       "         Q      S  Age_8  embarked_C  \n",
       "0    False   True  False       False  \n",
       "1    False  False  False        True  \n",
       "2    False   True  False       False  \n",
       "3    False   True  False       False  \n",
       "4    False   True  False       False  \n",
       "..     ...    ...    ...         ...  \n",
       "886  False   True  False       False  \n",
       "887  False   True  False       False  \n",
       "888  False   True  False       False  \n",
       "889  False  False  False        True  \n",
       "890   True  False  False       False  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adffc802",
   "metadata": {
    "papermill": {
     "duration": 0.036254,
     "end_time": "2024-08-03T04:39:26.645082",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.608828",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Fare-Survivorship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "009aff5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:26.720326Z",
     "iopub.status.busy": "2024-08-03T04:39:26.719512Z",
     "iopub.status.idle": "2024-08-03T04:39:26.732792Z",
     "shell.execute_reply": "2024-08-03T04:39:26.731561Z"
    },
    "papermill": {
     "duration": 0.053419,
     "end_time": "2024-08-03T04:39:26.735053",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.681634",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived     Fare\n",
       "0           0   7.2500\n",
       "1           1  71.2833\n",
       "2           1   7.9250\n",
       "3           1  53.1000\n",
       "4           0   8.0500\n",
       "..        ...      ...\n",
       "886         0  13.0000\n",
       "887         1  30.0000\n",
       "888         0  23.4500\n",
       "889         1  30.0000\n",
       "890         0   7.7500\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot[['Survived', 'Fare']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9554f4",
   "metadata": {
    "papermill": {
     "duration": 0.037657,
     "end_time": "2024-08-03T04:39:26.809511",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.771854",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Outliners handling with flooring and capping technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "711fc354",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:26.885098Z",
     "iopub.status.busy": "2024-08-03T04:39:26.884359Z",
     "iopub.status.idle": "2024-08-03T04:39:27.319114Z",
     "shell.execute_reply": "2024-08-03T04:39:27.317911Z"
    },
    "papermill": {
     "duration": 0.475356,
     "end_time": "2024-08-03T04:39:27.321635",
     "exception": false,
     "start_time": "2024-08-03T04:39:26.846279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fare Distribution')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_onehot['Fare'].hist(bins=50)\n",
    "plt.title('Fare Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2f3dd40d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:27.398662Z",
     "iopub.status.busy": "2024-08-03T04:39:27.398235Z",
     "iopub.status.idle": "2024-08-03T04:39:27.596995Z",
     "shell.execute_reply": "2024-08-03T04:39:27.595832Z"
    },
    "papermill": {
     "duration": 0.239894,
     "end_time": "2024-08-03T04:39:27.599376",
     "exception": false,
     "start_time": "2024-08-03T04:39:27.359482",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Box plot of fare price')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_onehot['Fare'].plot(kind='box')\n",
    "plt.title('Box plot of fare price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5b15abc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:27.677949Z",
     "iopub.status.busy": "2024-08-03T04:39:27.677462Z",
     "iopub.status.idle": "2024-08-03T04:39:27.690313Z",
     "shell.execute_reply": "2024-08-03T04:39:27.689054Z"
    },
    "papermill": {
     "duration": 0.054651,
     "end_time": "2024-08-03T04:39:27.692686",
     "exception": false,
     "start_time": "2024-08-03T04:39:27.638035",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot['Fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "38918f7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:27.772373Z",
     "iopub.status.busy": "2024-08-03T04:39:27.771983Z",
     "iopub.status.idle": "2024-08-03T04:39:27.939614Z",
     "shell.execute_reply": "2024-08-03T04:39:27.938010Z"
    },
    "papermill": {
     "duration": 0.20949,
     "end_time": "2024-08-03T04:39:27.942370",
     "exception": false,
     "start_time": "2024-08-03T04:39:27.732880",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-26.724 65.6344\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q1 = train_onehot['Fare'].quantile(0.25)\n",
    "q3 = train_onehot['Fare'].quantile(0.75)\n",
    "IQR = q3-q1\n",
    "whiskers = 1.5\n",
    "lower_bound = q1-whiskers*IQR\n",
    "upper_bound = q3+whiskers*IQR\n",
    "\n",
    "# flooring and capturing technique\n",
    "print(lower_bound, upper_bound)\n",
    "train_onehot['Fare'] = train_onehot['Fare'].where(train_onehot['Fare']<=upper_bound, upper_bound) # replace with upper_bound value if 'Fare' is greater than upper_bound\n",
    "train_onehot['Fare'] = train_onehot['Fare'].where(train_onehot['Fare']>=lower_bound, lower_bound) # replace with lower_bound value if 'Fare' is smaller than lower_bound\n",
    "train_onehot['Fare'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bbef7ba3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:28.021193Z",
     "iopub.status.busy": "2024-08-03T04:39:28.020730Z",
     "iopub.status.idle": "2024-08-03T04:39:28.321712Z",
     "shell.execute_reply": "2024-08-03T04:39:28.320253Z"
    },
    "papermill": {
     "duration": 0.343456,
     "end_time": "2024-08-03T04:39:28.324440",
     "exception": false,
     "start_time": "2024-08-03T04:39:27.980984",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fare Distribution after Outliner Treatment ')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_onehot['Fare'].hist(bins=30)\n",
    "plt.title('Fare Distribution after Outliner Treatment ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "df69a166",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:28.405031Z",
     "iopub.status.busy": "2024-08-03T04:39:28.404612Z",
     "iopub.status.idle": "2024-08-03T04:39:29.195135Z",
     "shell.execute_reply": "2024-08-03T04:39:29.193923Z"
    },
    "papermill": {
     "duration": 0.833303,
     "end_time": "2024-08-03T04:39:29.197503",
     "exception": false,
     "start_time": "2024-08-03T04:39:28.364200",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/1533651370.py:6: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_cut = train_onehot['Fare'].groupby(pd.cut(train_onehot['Fare'], bins=bin_samp)).count()\n",
      "/tmp/ipykernel_18/1533651370.py:6: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_cut = train_onehot['Fare'].groupby(pd.cut(train_onehot['Fare'], bins=bin_samp)).count()\n",
      "/tmp/ipykernel_18/1533651370.py:6: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_cut = train_onehot['Fare'].groupby(pd.cut(train_onehot['Fare'], bins=bin_samp)).count()\n",
      "/tmp/ipykernel_18/1533651370.py:6: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_cut = train_onehot['Fare'].groupby(pd.cut(train_onehot['Fare'], bins=bin_samp)).count()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select bin values\n",
    "plt.suptitle(\"Fare Distribution\", size=16)\n",
    "i = 1\n",
    "for bin_samp in [3, 5, 10, 15] :\n",
    "    plt.subplot(2,2,i)\n",
    "    fare_cut = train_onehot['Fare'].groupby(pd.cut(train_onehot['Fare'], bins=bin_samp)).count()\n",
    "    fare_cut.plot(kind='bar', figsize=(8,8))\n",
    "    plt.xticks([])\n",
    "    plt.xlabel('')\n",
    "    plt.title(f'bins = {bin_samp}')\n",
    "    i += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ea83561a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:29.278978Z",
     "iopub.status.busy": "2024-08-03T04:39:29.278234Z",
     "iopub.status.idle": "2024-08-03T04:39:30.276094Z",
     "shell.execute_reply": "2024-08-03T04:39:30.274703Z"
    },
    "papermill": {
     "duration": 1.041593,
     "end_time": "2024-08-03T04:39:30.278782",
     "exception": false,
     "start_time": "2024-08-03T04:39:29.237189",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/84392625.py:9: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_survived_cut = pd.concat([train_onehot[['Survived']], not_survived], axis=1).groupby(pd.cut(train_onehot['Fare'], bins=b[b_index])).sum()\n",
      "/tmp/ipykernel_18/84392625.py:9: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_survived_cut = pd.concat([train_onehot[['Survived']], not_survived], axis=1).groupby(pd.cut(train_onehot['Fare'], bins=b[b_index])).sum()\n",
      "/tmp/ipykernel_18/84392625.py:9: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_survived_cut = pd.concat([train_onehot[['Survived']], not_survived], axis=1).groupby(pd.cut(train_onehot['Fare'], bins=b[b_index])).sum()\n",
      "/tmp/ipykernel_18/84392625.py:9: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_survived_cut = pd.concat([train_onehot[['Survived']], not_survived], axis=1).groupby(pd.cut(train_onehot['Fare'], bins=b[b_index])).sum()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "fig.suptitle('Fare Distribution', size=16)\n",
    "b_index = 0\n",
    "b = [3, 5, 10, 15] # bins\n",
    "conditional_prob_of_fare_by_bins = []\n",
    "for i in range(2) :\n",
    "    for j in range(2):\n",
    "        #print((i,j)\n",
    "        fare_survived_cut = pd.concat([train_onehot[['Survived']], not_survived], axis=1).groupby(pd.cut(train_onehot['Fare'], bins=b[b_index])).sum()\n",
    "        fare_survived_cut.plot(kind='bar', color=['Green', 'Grey'], figsize=(12,12), ax=axes[i, j], xticks=[], xlabel='', title=f'bins = {b[b_index]}')\n",
    "        conditional_prob_of_fare_by_bins.append(fare_survived_cut['Survived']/(fare_survived_cut['Survived']+fare_survived_cut['Not Survived']))\n",
    "        b_index+=1                                                                                                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "30dc01ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:30.361705Z",
     "iopub.status.busy": "2024-08-03T04:39:30.361327Z",
     "iopub.status.idle": "2024-08-03T04:39:30.373225Z",
     "shell.execute_reply": "2024-08-03T04:39:30.372109Z"
    },
    "papermill": {
     "duration": 0.056672,
     "end_time": "2024-08-03T04:39:30.375883",
     "exception": false,
     "start_time": "2024-08-03T04:39:30.319211",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cond_prob @ bins = 3 Fare\n",
      "(-0.0656, 21.878]    0.278505\n",
      "(21.878, 43.756]     0.443243\n",
      "(43.756, 65.634]     0.649123\n",
      "dtype: float64\n",
      "cond_prob @ bins = 5 Fare\n",
      "(-0.0656, 13.127]    0.247664\n",
      "(13.127, 26.254]     0.431034\n",
      "(26.254, 39.381]     0.442308\n",
      "(39.381, 52.508]     0.305556\n",
      "(52.508, 65.634]     0.697987\n",
      "dtype: float64\n",
      "cond_prob @ bins = 10 Fare\n",
      "(-0.0656, 6.563]    0.045455\n",
      "(6.563, 13.127]     0.258621\n",
      "(13.127, 19.69]     0.435294\n",
      "(19.69, 26.254]     0.426966\n",
      "(26.254, 32.817]    0.450000\n",
      "(32.817, 39.381]    0.416667\n",
      "(39.381, 45.944]    0.285714\n",
      "(45.944, 52.508]    0.318182\n",
      "(52.508, 59.071]    0.807692\n",
      "(59.071, 65.634]    0.674797\n",
      "dtype: float64\n",
      "cond_prob @ bins = 15 Fare\n",
      "(-0.0656, 4.376]    0.062500\n",
      "(4.376, 8.751]      0.207483\n",
      "(8.751, 13.127]     0.372881\n",
      "(13.127, 17.503]    0.391304\n",
      "(17.503, 21.878]    0.421053\n",
      "(21.878, 26.254]    0.477612\n",
      "(26.254, 30.629]    0.483871\n",
      "(30.629, 35.005]    0.275862\n",
      "(35.005, 39.381]    0.615385\n",
      "(39.381, 43.756]    0.285714\n",
      "(43.756, 48.132]    0.000000\n",
      "(48.132, 52.508]    0.466667\n",
      "(52.508, 56.883]    0.750000\n",
      "(56.883, 61.259]    0.875000\n",
      "(61.259, 65.634]    0.677686\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('cond_prob @ bins = 3', conditional_prob_of_fare_by_bins[0]) \n",
    "print('cond_prob @ bins = 5', conditional_prob_of_fare_by_bins[1])\n",
    "print('cond_prob @ bins = 10', conditional_prob_of_fare_by_bins[2])\n",
    "print('cond_prob @ bins = 15', conditional_prob_of_fare_by_bins[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e41493f",
   "metadata": {
    "papermill": {
     "duration": 0.042268,
     "end_time": "2024-08-03T04:39:30.458669",
     "exception": false,
     "start_time": "2024-08-03T04:39:30.416401",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- bins = 3 : simplest cut with effective pattern classifier\n",
    "- bins = 5 : more complexity but still have granuation  and pattern\n",
    "- bins = 10 : coming lack of pattern in the middle of chart\n",
    "- bins = 15 : \n",
    "\n",
    "**- select bins = 5 to do feature engineering (may be better than bins=3 because of containing more detail but not too many)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6acb15ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:30.542487Z",
     "iopub.status.busy": "2024-08-03T04:39:30.541669Z",
     "iopub.status.idle": "2024-08-03T04:39:30.554930Z",
     "shell.execute_reply": "2024-08-03T04:39:30.554002Z"
    },
    "papermill": {
     "duration": 0.057579,
     "end_time": "2024-08-03T04:39:30.557225",
     "exception": false,
     "start_time": "2024-08-03T04:39:30.499646",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_18/556121883.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_survived_cut_5 = pd.concat([train_onehot[['Survived']], not_survived], axis=1).groupby(pd.cut(train_onehot['Fare'], bins=5)).sum()\n"
     ]
    }
   ],
   "source": [
    "fare_survived_cut_5 = pd.concat([train_onehot[['Survived']], not_survived], axis=1).groupby(pd.cut(train_onehot['Fare'], bins=5)).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "34ca4d23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:30.640684Z",
     "iopub.status.busy": "2024-08-03T04:39:30.639635Z",
     "iopub.status.idle": "2024-08-03T04:39:30.651774Z",
     "shell.execute_reply": "2024-08-03T04:39:30.650593Z"
    },
    "papermill": {
     "duration": 0.05631,
     "end_time": "2024-08-03T04:39:30.654072",
     "exception": false,
     "start_time": "2024-08-03T04:39:30.597762",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Not Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(-0.0656, 13.127]</th>\n",
       "      <td>106</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(13.127, 26.254]</th>\n",
       "      <td>75</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(26.254, 39.381]</th>\n",
       "      <td>46</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(39.381, 52.508]</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(52.508, 65.634]</th>\n",
       "      <td>104</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Survived  Not Survived\n",
       "Fare                                     \n",
       "(-0.0656, 13.127]       106           322\n",
       "(13.127, 26.254]         75            99\n",
       "(26.254, 39.381]         46            58\n",
       "(39.381, 52.508]         11            25\n",
       "(52.508, 65.634]        104            45"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare_survived_cut_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "36253327",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:30.738049Z",
     "iopub.status.busy": "2024-08-03T04:39:30.737004Z",
     "iopub.status.idle": "2024-08-03T04:39:30.754942Z",
     "shell.execute_reply": "2024-08-03T04:39:30.753668Z"
    },
    "papermill": {
     "duration": 0.062653,
     "end_time": "2024-08-03T04:39:30.757395",
     "exception": false,
     "start_time": "2024-08-03T04:39:30.694742",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def fare_reassign(x):\n",
    "    if x['Fare'] <= 13.127 : return 1\n",
    "    elif x['Fare'] <= 26.254 : return 2\n",
    "    elif x['Fare'] <= 39.381 : return 3\n",
    "    elif x['Fare'] <= 52.508 : return 4\n",
    "    else : return 5\n",
    "train_onehot['Fare'] = train_onehot.apply(fare_reassign, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "df89f776",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:30.843945Z",
     "iopub.status.busy": "2024-08-03T04:39:30.842935Z",
     "iopub.status.idle": "2024-08-03T04:39:30.862182Z",
     "shell.execute_reply": "2024-08-03T04:39:30.860987Z"
    },
    "papermill": {
     "duration": 0.065319,
     "end_time": "2024-08-03T04:39:30.864395",
     "exception": false,
     "start_time": "2024-08-03T04:39:30.799076",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>Age_8</th>\n",
       "      <th>embarked_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age  SibSp  Parch  Fare  female   male      C      Q  \\\n",
       "0           0      -1  22.0      1      0     1   False   True  False  False   \n",
       "1           1       1  38.0      1      0     5    True  False   True  False   \n",
       "2           1      -1  26.0      0      0     1    True  False  False  False   \n",
       "3           1       1  35.0      1      0     5    True  False  False  False   \n",
       "4           0      -1  35.0      0      0     1   False   True  False  False   \n",
       "..        ...     ...   ...    ...    ...   ...     ...    ...    ...    ...   \n",
       "886         0       0  27.0      0      0     1   False   True  False  False   \n",
       "887         1       1  19.0      0      0     3    True  False  False  False   \n",
       "888         0      -1  28.0      1      2     2    True  False  False  False   \n",
       "889         1       1  26.0      0      0     3   False   True   True  False   \n",
       "890         0      -1  32.0      0      0     1   False   True  False   True   \n",
       "\n",
       "         S  Age_8  embarked_C  \n",
       "0     True  False       False  \n",
       "1    False  False        True  \n",
       "2     True  False       False  \n",
       "3     True  False       False  \n",
       "4     True  False       False  \n",
       "..     ...    ...         ...  \n",
       "886   True  False       False  \n",
       "887   True  False       False  \n",
       "888   True  False       False  \n",
       "889  False  False        True  \n",
       "890  False  False       False  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ef58a6",
   "metadata": {
    "papermill": {
     "duration": 0.04077,
     "end_time": "2024-08-03T04:39:30.945894",
     "exception": false,
     "start_time": "2024-08-03T04:39:30.905124",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### SibSp-Survivorship, Parch-Survivorship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "41d51624",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:31.030473Z",
     "iopub.status.busy": "2024-08-03T04:39:31.029432Z",
     "iopub.status.idle": "2024-08-03T04:39:31.041088Z",
     "shell.execute_reply": "2024-08-03T04:39:31.040008Z"
    },
    "papermill": {
     "duration": 0.056731,
     "end_time": "2024-08-03T04:39:31.043646",
     "exception": false,
     "start_time": "2024-08-03T04:39:30.986915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sibsp_parch_survivorship_df = pd.concat([train_onehot[['Survived', 'SibSp']], not_survived.astype(int)], axis=1)\n",
    "sibsp_survived = sibsp_parch_survivorship_df.groupby('SibSp').sum()\n",
    "sibsp_survived_prob_cond = sibsp_survived.copy()\n",
    "sibsp_survived_prob_cond.insert(column='P(Survived|SibSp=n)', value=sibsp_survived['Survived']/(sibsp_survived['Survived']+sibsp_survived['Not Survived']), loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "0f0a93bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:31.129297Z",
     "iopub.status.busy": "2024-08-03T04:39:31.128858Z",
     "iopub.status.idle": "2024-08-03T04:39:31.140764Z",
     "shell.execute_reply": "2024-08-03T04:39:31.139347Z"
    },
    "papermill": {
     "duration": 0.057219,
     "end_time": "2024-08-03T04:39:31.143198",
     "exception": false,
     "start_time": "2024-08-03T04:39:31.085979",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Not Survived</th>\n",
       "      <th>P(Survived|SibSp=n)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210</td>\n",
       "      <td>398</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>112</td>\n",
       "      <td>97</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived  Not Survived  P(Survived|SibSp=n)\n",
       "SibSp                                             \n",
       "0           210           398             0.345395\n",
       "1           112            97             0.535885\n",
       "2            13            15             0.464286\n",
       "3             4            12             0.250000\n",
       "4             3            15             0.166667\n",
       "5             0             5             0.000000\n",
       "8             0             7             0.000000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sibsp_survived_prob_cond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "53f80d63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:31.227829Z",
     "iopub.status.busy": "2024-08-03T04:39:31.226834Z",
     "iopub.status.idle": "2024-08-03T04:39:31.579788Z",
     "shell.execute_reply": "2024-08-03T04:39:31.578442Z"
    },
    "papermill": {
     "duration": 0.397982,
     "end_time": "2024-08-03T04:39:31.582247",
     "exception": false,
     "start_time": "2024-08-03T04:39:31.184265",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of SibSp')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sibsp_survived.plot(kind='bar', color=['Green', 'Grey'], rot=0)\n",
    "plt.title('SibSp-Survivorship')\n",
    "plt.xlabel('Number of SibSp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "99256b98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:31.668485Z",
     "iopub.status.busy": "2024-08-03T04:39:31.668091Z",
     "iopub.status.idle": "2024-08-03T04:39:31.679961Z",
     "shell.execute_reply": "2024-08-03T04:39:31.678941Z"
    },
    "papermill": {
     "duration": 0.057549,
     "end_time": "2024-08-03T04:39:31.682280",
     "exception": false,
     "start_time": "2024-08-03T04:39:31.624731",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sibsp_parch_survivorship_df = pd.concat([train_onehot[['Survived', 'Parch']], not_survived.astype(int)], axis=1)\n",
    "parch_survived = sibsp_parch_survivorship_df.groupby('Parch').sum()\n",
    "parch_survived_prob_cond = parch_survived.copy() \n",
    "parch_survived_prob_cond.insert(column='P(Survived|Parch=n)', value=parch_survived['Survived']/(parch_survived['Survived']+parch_survived['Not Survived']), loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1a6803cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:31.767099Z",
     "iopub.status.busy": "2024-08-03T04:39:31.766685Z",
     "iopub.status.idle": "2024-08-03T04:39:31.778013Z",
     "shell.execute_reply": "2024-08-03T04:39:31.776975Z"
    },
    "papermill": {
     "duration": 0.056466,
     "end_time": "2024-08-03T04:39:31.780363",
     "exception": false,
     "start_time": "2024-08-03T04:39:31.723897",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Not Survived</th>\n",
       "      <th>P(Survived|Parch=n)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>233</td>\n",
       "      <td>445</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>53</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived  Not Survived  P(Survived|Parch=n)\n",
       "Parch                                             \n",
       "0           233           445             0.343658\n",
       "1            65            53             0.550847\n",
       "2            40            40             0.500000\n",
       "3             3             2             0.600000\n",
       "4             0             4             0.000000\n",
       "5             1             4             0.200000\n",
       "6             0             1             0.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parch_survived_prob_cond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2e71a252",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:31.866890Z",
     "iopub.status.busy": "2024-08-03T04:39:31.866322Z",
     "iopub.status.idle": "2024-08-03T04:39:32.140965Z",
     "shell.execute_reply": "2024-08-03T04:39:32.139813Z"
    },
    "papermill": {
     "duration": 0.320632,
     "end_time": "2024-08-03T04:39:32.143324",
     "exception": false,
     "start_time": "2024-08-03T04:39:31.822692",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of Parch')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parch_survived.plot(kind='bar', color=['Green', 'Grey'], rot=0)\n",
    "plt.title('Parch-Survivorship')\n",
    "plt.xlabel('Number of Parch')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39aae151",
   "metadata": {
    "papermill": {
     "duration": 0.042035,
     "end_time": "2024-08-03T04:39:32.227390",
     "exception": false,
     "start_time": "2024-08-03T04:39:32.185355",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**- Pattern of SibSp and Parch are nearly the same, we can combine them to one feature called family size feature**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f2b3c1",
   "metadata": {
    "papermill": {
     "duration": 0.042032,
     "end_time": "2024-08-03T04:39:32.311379",
     "exception": false,
     "start_time": "2024-08-03T04:39:32.269347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### FamilySize-Survivorship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "beb626ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:32.399081Z",
     "iopub.status.busy": "2024-08-03T04:39:32.398647Z",
     "iopub.status.idle": "2024-08-03T04:39:32.406439Z",
     "shell.execute_reply": "2024-08-03T04:39:32.405313Z"
    },
    "papermill": {
     "duration": 0.055479,
     "end_time": "2024-08-03T04:39:32.409128",
     "exception": false,
     "start_time": "2024-08-03T04:39:32.353649",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_onehot['FamilySize'] = train_onehot['Parch'] + train_onehot['SibSp']\n",
    "train_onehot = train_onehot.drop(columns=['SibSp', 'Parch'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "94600e0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:32.497161Z",
     "iopub.status.busy": "2024-08-03T04:39:32.496760Z",
     "iopub.status.idle": "2024-08-03T04:39:32.513101Z",
     "shell.execute_reply": "2024-08-03T04:39:32.511955Z"
    },
    "papermill": {
     "duration": 0.063969,
     "end_time": "2024-08-03T04:39:32.515541",
     "exception": false,
     "start_time": "2024-08-03T04:39:32.451572",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>Age_8</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  Fare  female   male      C      Q      S  Age_8  \\\n",
       "0         0      -1  22.0     1   False   True  False  False   True  False   \n",
       "1         1       1  38.0     5    True  False   True  False  False  False   \n",
       "2         1      -1  26.0     1    True  False  False  False   True  False   \n",
       "3         1       1  35.0     5    True  False  False  False   True  False   \n",
       "4         0      -1  35.0     1   False   True  False  False   True  False   \n",
       "\n",
       "   embarked_C  FamilySize  \n",
       "0       False           1  \n",
       "1        True           1  \n",
       "2       False           0  \n",
       "3       False           1  \n",
       "4       False           0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "25528356",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:32.603426Z",
     "iopub.status.busy": "2024-08-03T04:39:32.602997Z",
     "iopub.status.idle": "2024-08-03T04:39:32.616031Z",
     "shell.execute_reply": "2024-08-03T04:39:32.614657Z"
    },
    "papermill": {
     "duration": 0.059536,
     "end_time": "2024-08-03T04:39:32.618619",
     "exception": false,
     "start_time": "2024-08-03T04:39:32.559083",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "famsize_survivorship_df = pd.concat([train_onehot[['Survived', 'FamilySize']], not_survived.astype(int)], axis=1)\n",
    "famsize_survived_group = famsize_survivorship_df.groupby('FamilySize').sum()\n",
    "famsize_survived_prob_cond = famsize_survived_group.copy() \n",
    "famsize_survived_prob_cond.insert(column='P(Survived|FamSize=n)', value=famsize_survived_group['Survived']/(famsize_survived_group['Survived']+famsize_survived_group['Not Survived']), loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8d25a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:32.707814Z",
     "iopub.status.busy": "2024-08-03T04:39:32.707066Z",
     "iopub.status.idle": "2024-08-03T04:39:32.718498Z",
     "shell.execute_reply": "2024-08-03T04:39:32.717303Z"
    },
    "papermill": {
     "duration": 0.059252,
     "end_time": "2024-08-03T04:39:32.720688",
     "exception": false,
     "start_time": "2024-08-03T04:39:32.661436",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Not Survived</th>\n",
       "      <th>P(Survived|FamSize=n)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FamilySize</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>163</td>\n",
       "      <td>374</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>72</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59</td>\n",
       "      <td>43</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Survived  Not Survived  P(Survived|FamSize=n)\n",
       "FamilySize                                               \n",
       "0                163           374               0.303538\n",
       "1                 89            72               0.552795\n",
       "2                 59            43               0.578431\n",
       "3                 21             8               0.724138\n",
       "4                  3            12               0.200000\n",
       "5                  3            19               0.136364\n",
       "6                  4             8               0.333333\n",
       "7                  0             6               0.000000\n",
       "10                 0             7               0.000000"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "famsize_survived_prob_cond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "735db1a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:32.807114Z",
     "iopub.status.busy": "2024-08-03T04:39:32.806717Z",
     "iopub.status.idle": "2024-08-03T04:39:33.127448Z",
     "shell.execute_reply": "2024-08-03T04:39:33.126224Z"
    },
    "papermill": {
     "duration": 0.366867,
     "end_time": "2024-08-03T04:39:33.130131",
     "exception": false,
     "start_time": "2024-08-03T04:39:32.763264",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='FamilySize'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "famsize_survived_group.plot(kind='bar', color=['Green', 'Grey'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "25b55290",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:33.219449Z",
     "iopub.status.busy": "2024-08-03T04:39:33.219042Z",
     "iopub.status.idle": "2024-08-03T04:39:33.489508Z",
     "shell.execute_reply": "2024-08-03T04:39:33.488400Z"
    },
    "papermill": {
     "duration": 0.318111,
     "end_time": "2024-08-03T04:39:33.491720",
     "exception": false,
     "start_time": "2024-08-03T04:39:33.173609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Survival Rate')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "famsize_survived_prob_cond['P(Survived|FamSize=n)'].plot(kind='bar')\n",
    "plt.title('Survival Rate by Family Size')\n",
    "plt.ylabel('Survival Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f759df07",
   "metadata": {
    "papermill": {
     "duration": 0.043469,
     "end_time": "2024-08-03T04:39:33.580179",
     "exception": false,
     "start_time": "2024-08-03T04:39:33.536710",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Family Size = 3 , highest survival rate\n",
    "- Famliy Size > 3, survival rate tends to be lower\n",
    "- Family Size > 0 and < 3 , survival rate  tends to be higher\n",
    "\n",
    "- -**cut family size into 3 groups : 0-3, =3 (remarkable fam_size), >3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "114c6eee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:33.669252Z",
     "iopub.status.busy": "2024-08-03T04:39:33.668817Z",
     "iopub.status.idle": "2024-08-03T04:39:33.683342Z",
     "shell.execute_reply": "2024-08-03T04:39:33.682303Z"
    },
    "papermill": {
     "duration": 0.061572,
     "end_time": "2024-08-03T04:39:33.685607",
     "exception": false,
     "start_time": "2024-08-03T04:39:33.624035",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def fam_size_assign(x) :\n",
    "    if x['FamilySize'] < 3 : return -1\n",
    "    elif x['FamilySize'] == 3: return 1\n",
    "    else : return 0\n",
    "train_onehot['FamilySize'] = train_onehot.apply(fam_size_assign, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "669ec70c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:33.774598Z",
     "iopub.status.busy": "2024-08-03T04:39:33.774180Z",
     "iopub.status.idle": "2024-08-03T04:39:33.794484Z",
     "shell.execute_reply": "2024-08-03T04:39:33.793467Z"
    },
    "papermill": {
     "duration": 0.067653,
     "end_time": "2024-08-03T04:39:33.796784",
     "exception": false,
     "start_time": "2024-08-03T04:39:33.729131",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>Age_8</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age  Fare  female   male      C      Q      S  Age_8  \\\n",
       "0           0      -1  22.0     1   False   True  False  False   True  False   \n",
       "1           1       1  38.0     5    True  False   True  False  False  False   \n",
       "2           1      -1  26.0     1    True  False  False  False   True  False   \n",
       "3           1       1  35.0     5    True  False  False  False   True  False   \n",
       "4           0      -1  35.0     1   False   True  False  False   True  False   \n",
       "..        ...     ...   ...   ...     ...    ...    ...    ...    ...    ...   \n",
       "886         0       0  27.0     1   False   True  False  False   True  False   \n",
       "887         1       1  19.0     3    True  False  False  False   True  False   \n",
       "888         0      -1  28.0     2    True  False  False  False   True  False   \n",
       "889         1       1  26.0     3   False   True   True  False  False  False   \n",
       "890         0      -1  32.0     1   False   True  False   True  False  False   \n",
       "\n",
       "     embarked_C  FamilySize  \n",
       "0         False          -1  \n",
       "1          True          -1  \n",
       "2         False          -1  \n",
       "3         False          -1  \n",
       "4         False          -1  \n",
       "..          ...         ...  \n",
       "886       False          -1  \n",
       "887       False          -1  \n",
       "888       False           1  \n",
       "889        True          -1  \n",
       "890       False          -1  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "0e610a30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:33.887930Z",
     "iopub.status.busy": "2024-08-03T04:39:33.887513Z",
     "iopub.status.idle": "2024-08-03T04:39:33.904794Z",
     "shell.execute_reply": "2024-08-03T04:39:33.903584Z"
    },
    "papermill": {
     "duration": 0.064871,
     "end_time": "2024-08-03T04:39:33.907101",
     "exception": false,
     "start_time": "2024-08-03T04:39:33.842230",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Age_8</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Fare   male  Age_8  embarked_C  FamilySize\n",
       "0           0      -1     1   True  False       False          -1\n",
       "1           1       1     5  False  False        True          -1\n",
       "2           1      -1     1  False  False       False          -1\n",
       "3           1       1     5  False  False       False          -1\n",
       "4           0      -1     1   True  False       False          -1\n",
       "..        ...     ...   ...    ...    ...         ...         ...\n",
       "886         0       0     1   True  False       False          -1\n",
       "887         1       1     3  False  False       False          -1\n",
       "888         0      -1     2  False  False       False           1\n",
       "889         1       1     3   True  False        True          -1\n",
       "890         0      -1     1   True  False       False          -1\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_onehot_featured = train_onehot.drop(columns=['Age', 'C', 'Q', 'S', 'female'])\n",
    "train_onehot_featured"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37bd0883",
   "metadata": {
    "papermill": {
     "duration": 0.045525,
     "end_time": "2024-08-03T04:39:33.997817",
     "exception": false,
     "start_time": "2024-08-03T04:39:33.952292",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "423afd78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:34.090994Z",
     "iopub.status.busy": "2024-08-03T04:39:34.089966Z",
     "iopub.status.idle": "2024-08-03T04:39:34.140613Z",
     "shell.execute_reply": "2024-08-03T04:39:34.139494Z"
    },
    "papermill": {
     "duration": 0.10028,
     "end_time": "2024-08-03T04:39:34.143584",
     "exception": false,
     "start_time": "2024-08-03T04:39:34.043304",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.83417085, 0.74371859, 0.8030303 ])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = train_onehot_featured['Survived']\n",
    "X = train_onehot_featured.iloc[:, 1:]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.33)\n",
    "\n",
    "log_model2 = LogisticRegression(random_state=42, max_iter=300).fit(X_train, y_train)\n",
    "cross_val_score(log_model2, X_train, y_train, scoring='accuracy', cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "66240e30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:34.294546Z",
     "iopub.status.busy": "2024-08-03T04:39:34.294152Z",
     "iopub.status.idle": "2024-08-03T04:39:34.321000Z",
     "shell.execute_reply": "2024-08-03T04:39:34.319487Z"
    },
    "papermill": {
     "duration": 0.07677,
     "end_time": "2024-08-03T04:39:34.323767",
     "exception": false,
     "start_time": "2024-08-03T04:39:34.246997",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.85      0.85       374\n",
      "           1       0.75      0.75      0.75       222\n",
      "\n",
      "    accuracy                           0.81       596\n",
      "   macro avg       0.80      0.80      0.80       596\n",
      "weighted avg       0.81      0.81      0.81       596\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.83      0.83       175\n",
      "           1       0.75      0.76      0.76       120\n",
      "\n",
      "    accuracy                           0.80       295\n",
      "   macro avg       0.79      0.79      0.79       295\n",
      "weighted avg       0.80      0.80      0.80       295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_train_pred_model2 = log_model2.predict(X_train)\n",
    "y_test_pred_model2 = log_model2.predict(X_test)\n",
    "report2 = classification_report(y_train, y_train_pred_model2)\n",
    "report2_test = classification_report(y_test, y_test_pred_model2)\n",
    "print(report2)\n",
    "print(report2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b092a87",
   "metadata": {
    "papermill": {
     "duration": 0.044071,
     "end_time": "2024-08-03T04:39:34.412573",
     "exception": false,
     "start_time": "2024-08-03T04:39:34.368502",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "fc6e8cce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:34.507050Z",
     "iopub.status.busy": "2024-08-03T04:39:34.506220Z",
     "iopub.status.idle": "2024-08-03T04:39:34.694681Z",
     "shell.execute_reply": "2024-08-03T04:39:34.693621Z"
    },
    "papermill": {
     "duration": 0.239209,
     "end_time": "2024-08-03T04:39:34.697296",
     "exception": false,
     "start_time": "2024-08-03T04:39:34.458087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = train_onehot_featured['Survived']\n",
    "X = train_onehot_featured.iloc[:, 1:]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.33)\n",
    "\n",
    "rf2 = RandomForestClassifier(random_state=42).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "81d7f46a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:34.787684Z",
     "iopub.status.busy": "2024-08-03T04:39:34.787322Z",
     "iopub.status.idle": "2024-08-03T04:39:34.833872Z",
     "shell.execute_reply": "2024-08-03T04:39:34.832353Z"
    },
    "papermill": {
     "duration": 0.095039,
     "end_time": "2024-08-03T04:39:34.836474",
     "exception": false,
     "start_time": "2024-08-03T04:39:34.741435",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.95      0.88       374\n",
      "           1       0.89      0.67      0.76       222\n",
      "\n",
      "    accuracy                           0.84       596\n",
      "   macro avg       0.86      0.81      0.82       596\n",
      "weighted avg       0.85      0.84      0.84       596\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.91      0.83       175\n",
      "           1       0.82      0.60      0.69       120\n",
      "\n",
      "    accuracy                           0.78       295\n",
      "   macro avg       0.79      0.75      0.76       295\n",
      "weighted avg       0.79      0.78      0.78       295\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_train_pred_rf2 = rf2.predict(X_train)\n",
    "y_test_pred_rf2 = rf2.predict(X_test)\n",
    "report_rf2 = classification_report(y_train, y_train_pred_rf2)\n",
    "report_rf2_test = classification_report(y_test, y_test_pred_rf2)\n",
    "\n",
    "print(report_rf2)\n",
    "print(report_rf2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc73e473",
   "metadata": {
    "papermill": {
     "duration": 0.045222,
     "end_time": "2024-08-03T04:39:34.926513",
     "exception": false,
     "start_time": "2024-08-03T04:39:34.881291",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "b2c6397e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:35.018735Z",
     "iopub.status.busy": "2024-08-03T04:39:35.017931Z",
     "iopub.status.idle": "2024-08-03T04:39:35.026084Z",
     "shell.execute_reply": "2024-08-03T04:39:35.024931Z"
    },
    "papermill": {
     "duration": 0.057166,
     "end_time": "2024-08-03T04:39:35.029051",
     "exception": false,
     "start_time": "2024-08-03T04:39:34.971885",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- Train Data--\n",
      "Model: Logistic Regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.87      0.84       374\n",
      "           1       0.76      0.68      0.72       222\n",
      "\n",
      "    accuracy                           0.80       596\n",
      "   macro avg       0.79      0.77      0.78       596\n",
      "weighted avg       0.80      0.80      0.80       596\n",
      "\n",
      "-- Test Data--\n",
      "Model: Logistic Regression\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.87      0.85       175\n",
      "           1       0.80      0.72      0.76       120\n",
      "\n",
      "    accuracy                           0.81       295\n",
      "   macro avg       0.81      0.80      0.80       295\n",
      "weighted avg       0.81      0.81      0.81       295\n",
      "\n",
      "-------------------------\n",
      "-- Train Data--\n",
      "Model: Logistic Regression (after Feature Eng.)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.85      0.85       374\n",
      "           1       0.75      0.75      0.75       222\n",
      "\n",
      "    accuracy                           0.81       596\n",
      "   macro avg       0.80      0.80      0.80       596\n",
      "weighted avg       0.81      0.81      0.81       596\n",
      "\n",
      "-- Test Data--\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.83      0.83       175\n",
      "           1       0.75      0.76      0.76       120\n",
      "\n",
      "    accuracy                           0.80       295\n",
      "   macro avg       0.79      0.79      0.79       295\n",
      "weighted avg       0.80      0.80      0.80       295\n",
      "\n",
      "-------------------------\n",
      "-- Train Data--\n",
      "Model: Random Forest\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98       374\n",
      "           1       0.99      0.96      0.97       222\n",
      "\n",
      "    accuracy                           0.98       596\n",
      "   macro avg       0.98      0.98      0.98       596\n",
      "weighted avg       0.98      0.98      0.98       596\n",
      "\n",
      "-- Test Data--\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.82      0.81       175\n",
      "           1       0.72      0.69      0.71       120\n",
      "\n",
      "    accuracy                           0.77       295\n",
      "   macro avg       0.76      0.75      0.76       295\n",
      "weighted avg       0.76      0.77      0.77       295\n",
      "\n",
      "-------------------------\n",
      "Model: Random Forest (after Feature Eng.)\n",
      "-- Train Data--\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.95      0.88       374\n",
      "           1       0.89      0.67      0.76       222\n",
      "\n",
      "    accuracy                           0.84       596\n",
      "   macro avg       0.86      0.81      0.82       596\n",
      "weighted avg       0.85      0.84      0.84       596\n",
      "\n",
      "-- Test Data--\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.91      0.83       175\n",
      "           1       0.82      0.60      0.69       120\n",
      "\n",
      "    accuracy                           0.78       295\n",
      "   macro avg       0.79      0.75      0.76       295\n",
      "weighted avg       0.79      0.78      0.78       295\n",
      "\n",
      "-------------------------\n"
     ]
    }
   ],
   "source": [
    "print('-- Train Data--')\n",
    "print('Model: Logistic Regression')\n",
    "print(report1)\n",
    "print('-- Test Data--')\n",
    "print('Model: Logistic Regression')\n",
    "print(report1_test)\n",
    "print('-------------------------')\n",
    "print('-- Train Data--')\n",
    "print('Model: Logistic Regression (after Feature Eng.)')\n",
    "print(report2)\n",
    "print('-- Test Data--')\n",
    "print(report2_test)\n",
    "print('-------------------------')\n",
    "print('-- Train Data--')\n",
    "print('Model: Random Forest')\n",
    "print(report_rf1)\n",
    "print('-- Test Data--')\n",
    "print(report_rf1_test)\n",
    "print('-------------------------')\n",
    "print('Model: Random Forest (after Feature Eng.)')\n",
    "print('-- Train Data--')\n",
    "print(report_rf2)\n",
    "print('-- Test Data--')\n",
    "print(report_rf2_test)\n",
    "print('-------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2f36e76b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:35.122279Z",
     "iopub.status.busy": "2024-08-03T04:39:35.121354Z",
     "iopub.status.idle": "2024-08-03T04:39:35.139035Z",
     "shell.execute_reply": "2024-08-03T04:39:35.137972Z"
    },
    "papermill": {
     "duration": 0.066335,
     "end_time": "2024-08-03T04:39:35.141535",
     "exception": false,
     "start_time": "2024-08-03T04:39:35.075200",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auc log_model1 =  0.7745820686997158\n",
      "auc log_model2 =  0.7990075637134461\n",
      "auc rf1 =  0.9757190345425638\n",
      "auc rf2 =  0.8079322638146167\n"
     ]
    }
   ],
   "source": [
    "print('auc log_model1 = ', roc_auc_score(y_train, y_train_pred_model1))\n",
    "print('auc log_model2 = ', roc_auc_score(y_train, y_train_pred_model2))\n",
    "print('auc rf1 = ', roc_auc_score(y_train, y_train_pred_rf1))\n",
    "print('auc rf2 = ', roc_auc_score(y_train, y_train_pred_rf2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6258ec1b",
   "metadata": {
    "papermill": {
     "duration": 0.045214,
     "end_time": "2024-08-03T04:39:35.231968",
     "exception": false,
     "start_time": "2024-08-03T04:39:35.186754",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**!!! auc of random forest model is significantly lower after doing feature engineering, why?** (may be caused from simplifying feature -> minimizing overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e095d9",
   "metadata": {
    "papermill": {
     "duration": 0.045446,
     "end_time": "2024-08-03T04:39:35.324659",
     "exception": false,
     "start_time": "2024-08-03T04:39:35.279213",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a81f47a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:35.417064Z",
     "iopub.status.busy": "2024-08-03T04:39:35.416336Z",
     "iopub.status.idle": "2024-08-03T04:39:35.423251Z",
     "shell.execute_reply": "2024-08-03T04:39:35.422059Z"
    },
    "papermill": {
     "duration": 0.05506,
     "end_time": "2024-08-03T04:39:35.425426",
     "exception": false,
     "start_time": "2024-08-03T04:39:35.370366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "PassengerId = test['PassengerId']\n",
    "test = test.drop(columns=['PassengerId', 'Name', 'Ticket', 'Cabin'])\n",
    "test_model1_and_rf1 = test.copy()\n",
    "test_model2_and_rf2 = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5da34468",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:35.519025Z",
     "iopub.status.busy": "2024-08-03T04:39:35.518614Z",
     "iopub.status.idle": "2024-08-03T04:39:35.541091Z",
     "shell.execute_reply": "2024-08-03T04:39:35.539955Z"
    },
    "papermill": {
     "duration": 0.071674,
     "end_time": "2024-08-03T04:39:35.543443",
     "exception": false,
     "start_time": "2024-08-03T04:39:35.471769",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>3</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass   Age  SibSp  Parch      Fare  female   male      C      Q      S\n",
       "0         3  34.5      0      0    7.8292   False   True  False   True  False\n",
       "1         3  47.0      1      0    7.0000    True  False  False  False   True\n",
       "2         2  62.0      0      0    9.6875   False   True  False   True  False\n",
       "3         3  27.0      0      0    8.6625   False   True  False  False   True\n",
       "4         3  22.0      1      1   12.2875    True  False  False  False   True\n",
       "..      ...   ...    ...    ...       ...     ...    ...    ...    ...    ...\n",
       "413       3   NaN      0      0    8.0500   False   True  False  False   True\n",
       "414       1  39.0      0      0  108.9000    True  False   True  False  False\n",
       "415       3  38.5      0      0    7.2500   False   True  False  False   True\n",
       "416       3   NaN      0      0    8.0500   False   True  False  False   True\n",
       "417       3   NaN      1      1   22.3583   False   True   True  False  False\n",
       "\n",
       "[418 rows x 10 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_dum = pd.get_dummies(test_model1_and_rf1['Sex'])\n",
    "embarked_dum = pd.get_dummies(test_model1_and_rf1['Embarked'])\n",
    "test_model1_and_rf1 = pd.concat([test_model1_and_rf1, sex_dum, embarked_dum], axis=1)\n",
    "test_model1_and_rf1 = test_model1_and_rf1.drop(columns = ['Sex', 'Embarked'])\n",
    "test_model1_and_rf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5689e165",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:35.636366Z",
     "iopub.status.busy": "2024-08-03T04:39:35.635796Z",
     "iopub.status.idle": "2024-08-03T04:39:35.710588Z",
     "shell.execute_reply": "2024-08-03T04:39:35.709360Z"
    },
    "papermill": {
     "duration": 0.123825,
     "end_time": "2024-08-03T04:39:35.713101",
     "exception": false,
     "start_time": "2024-08-03T04:39:35.589276",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_8</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Fare  Age_8  embarked_C  FamilySize   male\n",
       "0        -1     1  False       False          -1   True\n",
       "1        -1     1  False       False          -1  False\n",
       "2         0     1  False       False          -1   True\n",
       "3        -1     1  False       False          -1   True\n",
       "4        -1     1  False       False          -1  False\n",
       "..      ...   ...    ...         ...         ...    ...\n",
       "413      -1     1  False       False          -1   True\n",
       "414       1     5  False        True          -1  False\n",
       "415      -1     1  False       False          -1   True\n",
       "416      -1     1  False       False          -1   True\n",
       "417      -1     2  False        True          -1   True\n",
       "\n",
       "[418 rows x 6 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_model2_and_rf2['Age_8'] = (test_model2_and_rf2['Age'] == 8)\n",
    "test_model2_and_rf2['embarked_C'] = (test_model2_and_rf2['Embarked'] == 'C')\n",
    "test_model2_and_rf2['FamilySize'] = test_model2_and_rf2['SibSp'] + test_model2_and_rf2['Parch']\n",
    "\n",
    "def class_reassign(x):\n",
    "    if x['Pclass'] == 2 : return 0\n",
    "    elif x['Pclass'] == 3 : return -1\n",
    "    else : return 1\n",
    "train_onehot['Pclass'] = train_onehot.apply(class_reassign, axis=1)\n",
    "\n",
    "def fare_reassign(x):\n",
    "    if x['Fare'] <= 13.127 : return 1\n",
    "    elif x['Fare'] <= 26.254 : return 2\n",
    "    elif x['Fare'] <= 39.381 : return 3\n",
    "    elif x['Fare'] <= 52.508 : return 4\n",
    "    else : return 5\n",
    "train_onehot['Fare'] = train_onehot.apply(fare_reassign, axis=1)\n",
    "\n",
    "def fam_size_reassign(x) :\n",
    "    if x['FamilySize'] < 3 : return -1\n",
    "    elif x['FamilySize'] == 3: return 1\n",
    "    else : return 0\n",
    "train_onehot['FamilySize'] = train_onehot.apply(fam_size_assign, axis=1)\n",
    "\n",
    "test_model2_and_rf2['FamilySize'] = test_model2_and_rf2.apply(fam_size_assign, axis=1)\n",
    "test_model2_and_rf2['Fare'] = test_model2_and_rf2.apply(fare_reassign, axis=1)\n",
    "test_model2_and_rf2['Pclass'] = test_model2_and_rf2.apply(class_reassign, axis=1)\n",
    "\n",
    "sex_dum = pd.get_dummies(test_model2_and_rf2['Sex'])\n",
    "test_model2_and_rf2 = pd.concat([test_model2_and_rf2, sex_dum], axis=1)\n",
    "test_model2_and_rf2 = test_model2_and_rf2.drop(columns=['Sex', 'Age', 'SibSp', 'Parch', 'Embarked', 'female'])\n",
    "\n",
    "test_model1_and_rf1 = test_model1_and_rf1.fillna(test_model1_and_rf1.median())\n",
    "test_model2_and_rf2 = test_model2_and_rf2.fillna(test_model2_and_rf2.median())\n",
    "\n",
    "test_model2_and_rf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "470253b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:35.806053Z",
     "iopub.status.busy": "2024-08-03T04:39:35.805645Z",
     "iopub.status.idle": "2024-08-03T04:39:35.812710Z",
     "shell.execute_reply": "2024-08-03T04:39:35.811477Z"
    },
    "papermill": {
     "duration": 0.056209,
     "end_time": "2024-08-03T04:39:35.814924",
     "exception": false,
     "start_time": "2024-08-03T04:39:35.758715",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_model2_and_rf2 = pd.DataFrame({'Pclass' : test_model2_and_rf2['Pclass'], 'Fare' : test_model2_and_rf2['Fare'], \n",
    "                                   'male' : test_model2_and_rf2['male'], 'Age_8' : test_model2_and_rf2['Age_8'], \n",
    "                                   'embarked_C' : test_model2_and_rf2['embarked_C'], 'FamilySize' : test_model2_and_rf2['FamilySize']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "40eb3824",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:35.909182Z",
     "iopub.status.busy": "2024-08-03T04:39:35.908637Z",
     "iopub.status.idle": "2024-08-03T04:39:35.923109Z",
     "shell.execute_reply": "2024-08-03T04:39:35.921931Z"
    },
    "papermill": {
     "duration": 0.063644,
     "end_time": "2024-08-03T04:39:35.925658",
     "exception": false,
     "start_time": "2024-08-03T04:39:35.862014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_test_pred_model1 = log_model1.predict(test_model1_and_rf1)\n",
    "y_test_pred_model2 = log_model2.predict(test_model2_and_rf2)\n",
    "y_test_pred_rf1 = log_model1.predict(test_model1_and_rf1)\n",
    "y_test_pred_rf2 = log_model2.predict(test_model2_and_rf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "568dde98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:36.018701Z",
     "iopub.status.busy": "2024-08-03T04:39:36.018309Z",
     "iopub.status.idle": "2024-08-03T04:39:36.026052Z",
     "shell.execute_reply": "2024-08-03T04:39:36.024791Z"
    },
    "papermill": {
     "duration": 0.056993,
     "end_time": "2024-08-03T04:39:36.028259",
     "exception": false,
     "start_time": "2024-08-03T04:39:35.971266",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_result_model1 = pd.DataFrame({'PassengerId': PassengerId, 'Survived': y_test_pred_model1})\n",
    "df_result_model2 = pd.DataFrame({'PassengerId': PassengerId, 'Survived': y_test_pred_model2})\n",
    "df_result_rf1 = pd.DataFrame({'PassengerId': PassengerId, 'Survived': y_test_pred_rf1})\n",
    "df_result_rf2 = pd.DataFrame({'PassengerId': PassengerId, 'Survived': y_test_pred_rf1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "25bfa33b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-03T04:39:36.121955Z",
     "iopub.status.busy": "2024-08-03T04:39:36.120869Z",
     "iopub.status.idle": "2024-08-03T04:39:36.133034Z",
     "shell.execute_reply": "2024-08-03T04:39:36.131929Z"
    },
    "papermill": {
     "duration": 0.061852,
     "end_time": "2024-08-03T04:39:36.135835",
     "exception": false,
     "start_time": "2024-08-03T04:39:36.073983",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_result_model1.to_csv('/kaggle/working/result_model1.csv', index=False)\n",
    "df_result_model2.to_csv('/kaggle/working/result_model2.csv', index=False)\n",
    "df_result_rf1.to_csv('/kaggle/working/result_rf1.csv', index=False)\n",
    "df_result_rf2.to_csv('/kaggle/working/result_rf2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ab18453",
   "metadata": {
    "papermill": {
     "duration": 0.046892,
     "end_time": "2024-08-03T04:39:36.229739",
     "exception": false,
     "start_time": "2024-08-03T04:39:36.182847",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "databundleVersionId": 26502,
     "sourceId": 3136,
     "sourceType": "competition"
    }
   ],
   "dockerImageVersionId": 30746,
   "isGpuEnabled": false,
   "isInternetEnabled": false,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 27.751458,
   "end_time": "2024-08-03T04:39:36.998101",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-08-03T04:39:09.246643",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}