titanic-survival-problem.ipynb

{
 "cells": [
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     "status": "completed"
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    "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",
    "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": "f4af807c",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2022-10-14T05:53:02.137794Z",
     "iopub.status.idle": "2022-10-14T05:53:41.398957Z",
     "shell.execute_reply": "2022-10-14T05:53:41.397209Z"
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     "start_time": "2022-10-14T05:53:02.117680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: tensorflow~=2.6 in /opt/conda/lib/python3.7/site-packages (from tensorflow_decision_forests) (2.6.4)\r\n",
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      "Requirement already satisfied: opt-einsum~=3.3.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow~=2.6->tensorflow_decision_forests) (3.3.0)\r\n",
      "Collecting h5py~=3.1.0\r\n",
      "  Downloading h5py-3.1.0-cp37-cp37m-manylinux1_x86_64.whl (4.0 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.0/4.0 MB\u001b[0m \u001b[31m36.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
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      "  Downloading typing_extensions-3.10.0.2-py3-none-any.whl (26 kB)\r\n",
      "Requirement already satisfied: wrapt~=1.12.1 in /opt/conda/lib/python3.7/site-packages (from tensorflow~=2.6->tensorflow_decision_forests) (1.12.1)\r\n",
      "Requirement already satisfied: tensorflow-estimator<2.7,>=2.6.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow~=2.6->tensorflow_decision_forests) (2.6.0)\r\n",
      "Requirement already satisfied: gast==0.4.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow~=2.6->tensorflow_decision_forests) (0.4.0)\r\n",
      "Collecting numpy\r\n",
      "  Downloading numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl (14.8 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.8/14.8 MB\u001b[0m \u001b[31m72.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
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      "Requirement already satisfied: keras-preprocessing~=1.1.2 in /opt/conda/lib/python3.7/site-packages (from tensorflow~=2.6->tensorflow_decision_forests) (1.1.2)\r\n",
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      "Collecting tensorboard<2.7,>=2.6.0\r\n",
      "  Downloading tensorboard-2.6.0-py3-none-any.whl (5.6 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m89.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
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      "Requirement already satisfied: requests<3,>=2.21.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (2.28.1)\r\n",
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      "Requirement already satisfied: werkzeug>=0.11.15 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (2.2.2)\r\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (59.8.0)\r\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (1.35.0)\r\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (1.8.1)\r\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (0.6.1)\r\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /opt/conda/lib/python3.7/site-packages (from tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (0.4.6)\r\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (4.8)\r\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (0.2.7)\r\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (4.2.4)\r\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (1.3.1)\r\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in /opt/conda/lib/python3.7/site-packages (from markdown>=2.6.8->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (4.12.0)\r\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (2.1.0)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (2022.6.15.2)\r\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (1.26.12)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (3.3)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.7/site-packages (from werkzeug>=0.11.15->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (2.1.1)\r\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (3.8.0)\r\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.7/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (0.4.8)\r\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.7,>=2.6.0->tensorflow~=2.6->tensorflow_decision_forests) (3.2.0)\r\n",
      "Installing collected packages: typing-extensions, numpy, h5py, tensorboard\r\n",
      "  Attempting uninstall: typing-extensions\r\n",
      "    Found existing installation: typing_extensions 4.3.0\r\n",
      "    Uninstalling typing_extensions-4.3.0:\r\n",
      "      Successfully uninstalled typing_extensions-4.3.0\r\n",
      "  Attempting uninstall: numpy\r\n",
      "    Found existing installation: numpy 1.21.6\r\n",
      "    Uninstalling numpy-1.21.6:\r\n",
      "      Successfully uninstalled numpy-1.21.6\r\n",
      "  Attempting uninstall: h5py\r\n",
      "    Found existing installation: h5py 3.7.0\r\n",
      "    Uninstalling h5py-3.7.0:\r\n",
      "      Successfully uninstalled h5py-3.7.0\r\n",
      "  Attempting uninstall: tensorboard\r\n",
      "    Found existing installation: tensorboard 2.10.0\r\n",
      "    Uninstalling tensorboard-2.10.0:\r\n",
      "      Successfully uninstalled tensorboard-2.10.0\r\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "tensorflow-io 0.21.0 requires tensorflow-io-gcs-filesystem==0.21.0, which is not installed.\r\n",
      "beatrix-jupyterlab 3.1.7 requires google-cloud-bigquery-storage, which is not installed.\r\n",
      "xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\r\n",
      "tfx-bsl 1.9.0 requires pyarrow<6,>=1, but you have pyarrow 8.0.0 which is incompatible.\r\n",
      "tfx-bsl 1.9.0 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,<3,>=1.15.5, but you have tensorflow 2.6.4 which is incompatible.\r\n",
      "tensorflow-transform 1.9.0 requires pyarrow<6,>=1, but you have pyarrow 8.0.0 which is incompatible.\r\n",
      "tensorflow-transform 1.9.0 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,<2.10,>=1.15.5, but you have tensorflow 2.6.4 which is incompatible.\r\n",
      "tensorflow-serving-api 2.9.0 requires tensorflow<3,>=2.9.0, but you have tensorflow 2.6.4 which is incompatible.\r\n",
      "pytorch-lightning 1.7.6 requires tensorboard>=2.9.1, but you have tensorboard 2.6.0 which is incompatible.\r\n",
      "pytorch-lightning 1.7.6 requires typing-extensions>=4.0.0, but you have typing-extensions 3.10.0.2 which is incompatible.\r\n",
      "pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.5.3 which is incompatible.\r\n",
      "pandas-profiling 3.1.0 requires markupsafe~=2.0.1, but you have markupsafe 2.1.1 which is incompatible.\r\n",
      "nnabla 1.30.0 requires numpy>=1.20.0, but you have numpy 1.19.5 which is incompatible.\r\n",
      "jax 0.3.17 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\r\n",
      "flax 0.6.0 requires rich~=11.1, but you have rich 12.1.0 which is incompatible.\r\n",
      "flax 0.6.0 requires typing-extensions>=4.1.1, but you have typing-extensions 3.10.0.2 which is incompatible.\r\n",
      "flake8 4.0.1 requires importlib-metadata<4.3; python_version < \"3.8\", but you have importlib-metadata 4.12.0 which is incompatible.\r\n",
      "featuretools 1.11.1 requires numpy>=1.21.0, but you have numpy 1.19.5 which is incompatible.\r\n",
      "cmdstanpy 1.0.7 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\r\n",
      "apache-beam 2.40.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.5.1 which is incompatible.\r\n",
      "apache-beam 2.40.0 requires pyarrow<8.0.0,>=0.15.1, but you have pyarrow 8.0.0 which is incompatible.\r\n",
      "allennlp 2.10.0 requires h5py>=3.6.0, but you have h5py 3.1.0 which is incompatible.\r\n",
      "allennlp 2.10.0 requires numpy>=1.21.4, but you have numpy 1.19.5 which is incompatible.\r\n",
      "allennlp 2.10.0 requires protobuf==3.20.0, but you have protobuf 3.19.4 which is incompatible.\r\n",
      "aioitertools 0.10.0 requires typing_extensions>=4.0; python_version < \"3.10\", but you have typing-extensions 3.10.0.2 which is incompatible.\r\n",
      "aiobotocore 2.4.0 requires botocore<1.27.60,>=1.27.59, but you have botocore 1.27.72 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0mSuccessfully installed h5py-3.1.0 numpy-1.19.5 tensorboard-2.6.0 typing-extensions-3.10.0.2\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0mCollecting wurlitzer\r\n",
      "  Downloading wurlitzer-3.0.2-py3-none-any.whl (7.3 kB)\r\n",
      "Installing collected packages: wurlitzer\r\n",
      "Successfully installed wurlitzer-3.0.2\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow_decision_forests\n",
    "# wurlitzer is needed for displaying wurlitzer\n",
    "!pip install wurlitzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4121649a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:41.443334Z",
     "iopub.status.busy": "2022-10-14T05:53:41.442994Z",
     "iopub.status.idle": "2022-10-14T05:53:48.132424Z",
     "shell.execute_reply": "2022-10-14T05:53:48.131082Z"
    },
    "papermill": {
     "duration": 6.713705,
     "end_time": "2022-10-14T05:53:48.135301",
     "exception": false,
     "start_time": "2022-10-14T05:53:41.421596",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Required imports\n",
    "import tensorflow_decision_forests as tfdf\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccc35195",
   "metadata": {
    "papermill": {
     "duration": 0.019153,
     "end_time": "2022-10-14T05:53:48.174212",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.155059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1692462",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:48.215788Z",
     "iopub.status.busy": "2022-10-14T05:53:48.214629Z",
     "iopub.status.idle": "2022-10-14T05:53:48.268205Z",
     "shell.execute_reply": "2022-10-14T05:53:48.266843Z"
    },
    "papermill": {
     "duration": 0.076455,
     "end_time": "2022-10-14T05:53:48.270142",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.193687",
     "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": [
    "load_data = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "testds = pd.read_csv('/kaggle/input/titanic/test.csv')\n",
    "load_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11a2d0b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:48.313106Z",
     "iopub.status.busy": "2022-10-14T05:53:48.312394Z",
     "iopub.status.idle": "2022-10-14T05:53:48.334158Z",
     "shell.execute_reply": "2022-10-14T05:53:48.332805Z"
    },
    "papermill": {
     "duration": 0.045377,
     "end_time": "2022-10-14T05:53:48.336308",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.290931",
     "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0af6064",
   "metadata": {
    "papermill": {
     "duration": 0.019664,
     "end_time": "2022-10-14T05:53:48.375896",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.356232",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e5ade3",
   "metadata": {
    "papermill": {
     "duration": 0.022618,
     "end_time": "2022-10-14T05:53:48.421887",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.399269",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* plot missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c28c0028",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:48.474223Z",
     "iopub.status.busy": "2022-10-14T05:53:48.473268Z",
     "iopub.status.idle": "2022-10-14T05:53:48.479090Z",
     "shell.execute_reply": "2022-10-14T05:53:48.477947Z"
    },
    "papermill": {
     "duration": 0.034015,
     "end_time": "2022-10-14T05:53:48.481426",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.447411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_missing_data(dataset, title):\n",
    "    fig, ax = plt.subplots(figsize=(5,5))\n",
    "    plt.title(title)\n",
    "    sns.heatmap(dataset.isnull(),cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b309fc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:48.523656Z",
     "iopub.status.busy": "2022-10-14T05:53:48.522239Z",
     "iopub.status.idle": "2022-10-14T05:53:48.843127Z",
     "shell.execute_reply": "2022-10-14T05:53:48.842068Z"
    },
    "papermill": {
     "duration": 0.344416,
     "end_time": "2022-10-14T05:53:48.845643",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.501227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_missing_data(load_data, \"Training Dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df43b98f",
   "metadata": {
    "papermill": {
     "duration": 0.02025,
     "end_time": "2022-10-14T05:53:48.887297",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.867047",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* passengers who survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53d3bf87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:48.929660Z",
     "iopub.status.busy": "2022-10-14T05:53:48.929340Z",
     "iopub.status.idle": "2022-10-14T05:53:48.937409Z",
     "shell.execute_reply": "2022-10-14T05:53:48.936082Z"
    },
    "papermill": {
     "duration": 0.031387,
     "end_time": "2022-10-14T05:53:48.939221",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.907834",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c14d4ede",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:48.982665Z",
     "iopub.status.busy": "2022-10-14T05:53:48.982293Z",
     "iopub.status.idle": "2022-10-14T05:53:48.991961Z",
     "shell.execute_reply": "2022-10-14T05:53:48.990652Z"
    },
    "papermill": {
     "duration": 0.034399,
     "end_time": "2022-10-14T05:53:48.994368",
     "exception": false,
     "start_time": "2022-10-14T05:53:48.959969",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.616162\n",
       "1    0.383838\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data['Survived'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac4bb1f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:49.038625Z",
     "iopub.status.busy": "2022-10-14T05:53:49.038049Z",
     "iopub.status.idle": "2022-10-14T05:53:49.044180Z",
     "shell.execute_reply": "2022-10-14T05:53:49.042831Z"
    },
    "papermill": {
     "duration": 0.031586,
     "end_time": "2022-10-14T05:53:49.046898",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.015312",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def bar_chart_stacked(dataset, feature, stacked=True):\n",
    "    survived = dataset[dataset[\"Survived\"]==1][feature].value_counts()\n",
    "    dead = dataset[dataset['Survived']==0][feature].value_counts()\n",
    "    df_survived_dead = pd.DataFrame([survived,dead])\n",
    "    df_survived_dead.index = ['Passengers Survived','Passengers Died']\n",
    "    ax = df_survived_dead.plot(kind='bar',stacked=stacked,figsize=(5,5))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9039d76b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:49.090036Z",
     "iopub.status.busy": "2022-10-14T05:53:49.089371Z",
     "iopub.status.idle": "2022-10-14T05:53:49.260229Z",
     "shell.execute_reply": "2022-10-14T05:53:49.259132Z"
    },
    "papermill": {
     "duration": 0.194448,
     "end_time": "2022-10-14T05:53:49.262230",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.067782",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_stacked(load_data, \"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "135636c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:49.304907Z",
     "iopub.status.busy": "2022-10-14T05:53:49.304568Z",
     "iopub.status.idle": "2022-10-14T05:53:49.313957Z",
     "shell.execute_reply": "2022-10-14T05:53:49.312949Z"
    },
    "papermill": {
     "duration": 0.033069,
     "end_time": "2022-10-14T05:53:49.315937",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.282868",
     "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>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sex\n",
       "male    577\n",
       "female  314"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data['Sex'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6d749e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:49.358496Z",
     "iopub.status.busy": "2022-10-14T05:53:49.358159Z",
     "iopub.status.idle": "2022-10-14T05:53:49.369403Z",
     "shell.execute_reply": "2022-10-14T05:53:49.368109Z"
    },
    "papermill": {
     "duration": 0.035494,
     "end_time": "2022-10-14T05:53:49.371930",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.336436",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data.groupby('Sex').Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "43adbb93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:49.415278Z",
     "iopub.status.busy": "2022-10-14T05:53:49.414838Z",
     "iopub.status.idle": "2022-10-14T05:53:49.576501Z",
     "shell.execute_reply": "2022-10-14T05:53:49.574777Z"
    },
    "papermill": {
     "duration": 0.186358,
     "end_time": "2022-10-14T05:53:49.579309",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.392951",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_stacked(load_data, 'Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca3aed87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:49.624158Z",
     "iopub.status.busy": "2022-10-14T05:53:49.623335Z",
     "iopub.status.idle": "2022-10-14T05:53:49.645528Z",
     "shell.execute_reply": "2022-10-14T05:53:49.644027Z"
    },
    "papermill": {
     "duration": 0.046711,
     "end_time": "2022-10-14T05:53:49.647960",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.601249",
     "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>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass      1   2    3\n",
       "Survived              \n",
       "0          80  97  372\n",
       "1         136  87  119"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(load_data, index=\"Survived\", columns=\"Pclass\", values='Ticket', aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0dea9f91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:49.693019Z",
     "iopub.status.busy": "2022-10-14T05:53:49.692626Z",
     "iopub.status.idle": "2022-10-14T05:53:49.704429Z",
     "shell.execute_reply": "2022-10-14T05:53:49.702769Z"
    },
    "papermill": {
     "duration": 0.037493,
     "end_time": "2022-10-14T05:53:49.706861",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.669368",
     "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data.groupby(['Pclass']).Survived.mean().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e37a61d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:49.751241Z",
     "iopub.status.busy": "2022-10-14T05:53:49.750864Z",
     "iopub.status.idle": "2022-10-14T05:53:49.914001Z",
     "shell.execute_reply": "2022-10-14T05:53:49.912737Z"
    },
    "papermill": {
     "duration": 0.187599,
     "end_time": "2022-10-14T05:53:49.916118",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.728519",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_stacked(load_data, 'Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1bc7df8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:49.960870Z",
     "iopub.status.busy": "2022-10-14T05:53:49.960481Z",
     "iopub.status.idle": "2022-10-14T05:53:49.966941Z",
     "shell.execute_reply": "2022-10-14T05:53:49.965799Z"
    },
    "papermill": {
     "duration": 0.031397,
     "end_time": "2022-10-14T05:53:49.969043",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.937646",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def bar_chart_compare(dataset, feature1, feature2=None, title=None):\n",
    "    plt.figure(figsize = [5,5])\n",
    "    plt.title(title)\n",
    "    g = sns.barplot(x=feature1, y='Survived', hue=feature2, ci=None, data=dataset).set_ylabel('Survival rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9fda72d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:50.014056Z",
     "iopub.status.busy": "2022-10-14T05:53:50.013670Z",
     "iopub.status.idle": "2022-10-14T05:53:50.175336Z",
     "shell.execute_reply": "2022-10-14T05:53:50.174479Z"
    },
    "papermill": {
     "duration": 0.186813,
     "end_time": "2022-10-14T05:53:50.177398",
     "exception": false,
     "start_time": "2022-10-14T05:53:49.990585",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_compare(load_data, \"Pclass\", \"Sex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b233ac34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:50.223633Z",
     "iopub.status.busy": "2022-10-14T05:53:50.223301Z",
     "iopub.status.idle": "2022-10-14T05:53:50.236176Z",
     "shell.execute_reply": "2022-10-14T05:53:50.234973Z"
    },
    "papermill": {
     "duration": 0.03839,
     "end_time": "2022-10-14T05:53:50.238338",
     "exception": false,
     "start_time": "2022-10-14T05:53:50.199948",
     "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></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>female</th>\n",
       "      <td>0.921053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.157407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>female</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Survived\n",
       "Pclass Sex             \n",
       "1      female  0.968085\n",
       "       male    0.368852\n",
       "2      female  0.921053\n",
       "       male    0.157407\n",
       "3      female  0.500000\n",
       "       male    0.135447"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data.groupby(['Pclass', 'Sex']).Survived.mean().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a331704",
   "metadata": {
    "papermill": {
     "duration": 0.021911,
     "end_time": "2022-10-14T05:53:50.282916",
     "exception": false,
     "start_time": "2022-10-14T05:53:50.261005",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* function to plot histogram and kernel density estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "04ea5ccc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:50.330284Z",
     "iopub.status.busy": "2022-10-14T05:53:50.329019Z",
     "iopub.status.idle": "2022-10-14T05:53:50.337064Z",
     "shell.execute_reply": "2022-10-14T05:53:50.336251Z"
    },
    "papermill": {
     "duration": 0.033752,
     "end_time": "2022-10-14T05:53:50.339134",
     "exception": false,
     "start_time": "2022-10-14T05:53:50.305382",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_distribution(dataset, feature, title, bins=30, hist=True, fsize= (5,5)):\n",
    "    fig, ax = plt.subplots(figsize=fsize)\n",
    "    ax.set_title(title)\n",
    "    sns.distplot(load_data[feature], color='g', bins=bins, ax=ax)\n",
    "    \n",
    "def plot_kernel_density_estimate_survivors(dataset, feature1, title, fsize=(5,5)):\n",
    "    fig, ax = plt.subplots(figsize=fsize)\n",
    "    ax.set_title(title)\n",
    "    sns.kdeplot(dataset[feature1].loc[load_data[\"Survived\"]==1],\n",
    "               shade=True, ax=ax, label='Survived').set_xlabel(feature1)\n",
    "    sns.kdeplot(dataset[feature1].loc[load_data[\"Survived\"]==0],\n",
    "               shade=True, ax=ax, label=\"Died\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bfe21310",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:50.385311Z",
     "iopub.status.busy": "2022-10-14T05:53:50.384902Z",
     "iopub.status.idle": "2022-10-14T05:53:50.620672Z",
     "shell.execute_reply": "2022-10-14T05:53:50.619504Z"
    },
    "papermill": {
     "duration": 0.262377,
     "end_time": "2022-10-14T05:53:50.623581",
     "exception": false,
     "start_time": "2022-10-14T05:53:50.361204",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(load_data, \"Age\", \"Age Distribution Passengers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7089e1a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:50.671031Z",
     "iopub.status.busy": "2022-10-14T05:53:50.670210Z",
     "iopub.status.idle": "2022-10-14T05:53:50.853967Z",
     "shell.execute_reply": "2022-10-14T05:53:50.852869Z"
    },
    "papermill": {
     "duration": 0.210013,
     "end_time": "2022-10-14T05:53:50.856308",
     "exception": false,
     "start_time": "2022-10-14T05:53:50.646295",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_kernel_density_estimate_survivors(load_data, \"Age\", \"Age Distribution survived vs Died\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "abbb0564",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:50.904159Z",
     "iopub.status.busy": "2022-10-14T05:53:50.903125Z",
     "iopub.status.idle": "2022-10-14T05:53:50.910282Z",
     "shell.execute_reply": "2022-10-14T05:53:50.908911Z"
    },
    "papermill": {
     "duration": 0.033382,
     "end_time": "2022-10-14T05:53:50.912979",
     "exception": false,
     "start_time": "2022-10-14T05:53:50.879597",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_swarm_survivors(dataset, feature1, feature2, title, fsize= (155)):\n",
    "    fig, ax = plt.subplots(figsize=(18,5))\n",
    "    # Turns off grid on the left Axis\n",
    "    ax.grid(True)\n",
    "    plt.xticks(list(range(0,100,2)))\n",
    "    sns.swarmplot(y=feature1, x=feature2, hue='Survived', data=load_data).set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1debaec3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:50.959393Z",
     "iopub.status.busy": "2022-10-14T05:53:50.959017Z",
     "iopub.status.idle": "2022-10-14T05:53:51.582952Z",
     "shell.execute_reply": "2022-10-14T05:53:51.581470Z"
    },
    "papermill": {
     "duration": 0.650985,
     "end_time": "2022-10-14T05:53:51.586476",
     "exception": false,
     "start_time": "2022-10-14T05:53:50.935491",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_swarm_survivors(load_data, \"Sex\", \"Age\", \"Survivor Swarmplot for Age and Gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d26454fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:51.643164Z",
     "iopub.status.busy": "2022-10-14T05:53:51.642205Z",
     "iopub.status.idle": "2022-10-14T05:53:52.235340Z",
     "shell.execute_reply": "2022-10-14T05:53:52.234523Z"
    },
    "papermill": {
     "duration": 0.620697,
     "end_time": "2022-10-14T05:53:52.237207",
     "exception": false,
     "start_time": "2022-10-14T05:53:51.616510",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_swarm_survivors(load_data, \"Age\", \"Pclass\", \"Survivor Swarmplot for Age and Gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f1ec4c74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:52.289306Z",
     "iopub.status.busy": "2022-10-14T05:53:52.288921Z",
     "iopub.status.idle": "2022-10-14T05:53:52.506776Z",
     "shell.execute_reply": "2022-10-14T05:53:52.505435Z"
    },
    "papermill": {
     "duration": 0.247199,
     "end_time": "2022-10-14T05:53:52.509062",
     "exception": false,
     "start_time": "2022-10-14T05:53:52.261863",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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FHI6kk8hNDywZxLrLgGUA8+bNS7mygVnbuJbjpx7PzLqZjKscx8PbHi5pPWaWrSxHrFuAuQWP5yTL+mwjqRKoB3Ymj+cAtwGXRcSGgvZz+ukTgIi4MSIWR8TihoaGIb6Uwdu3fx/b27Zz3JTjGKMxzJk4h4dfcLCalbMsg3UVsFDSMZKqgIuAFb3arCC3cwrgAuDeiAhJk4DbgeUR8Zt844jYBrRIelVyNMBlwI8zfA1Dtqt9FwDTaqYBMG/iPB7d/ij7e/aXsiwzy1BmwZrMmV4J3AE8CXw/ItZKulbSO5Nm3wSmSloPfAzIH5J1JbAAuFrSI8ltevLcR4BvAOuBDcDPsnoNadi5dycAU2tzU8dz6ufQ1tXG+l3rS1mWmWUo0znWiFgJrOy17OqC+x3AhX2s91ngs4foczVwcrqVZmdney5Yp9RMAWDuxNzsyKPbH+WEaSeUrC4zy47PvMrYzvadjNEYJo2bBEBDbW6+d2PzxtIVZWaZKipYJf2HpLdJchAP0K69u5hSM4UxyY+uZmwN9dX1PL/7+RJXZmZZKTYovwL8d+BpSf8oyZ9hi7SzfeeBaYC8efXzeL7FwWpWrooK1oi4OyIuAV4JbATulvRbSe+XNDbLAke7ne07mVpz8DkP8+rnecRqVsaK/mgvaSrwZ8AHgIeBfyIXtHdlUlkZ6O7pZnfHbger2RGmqKMCJN0GnEDuYijvSI4nBbhV0uqsihvtmtqbCIIptQdPBRxdfzS72nfRuq+VCVUTSlSdmWWl2MOtvp4cOnWApOqI6IyIxRnUVRbyJwf0NccKsGn3Jl7W8LJhr8vMslXsVEBfx5T+Ls1CytGefXsAmFg18aDl+WD1dIBZeTrsiFXSDHJXj6qR9Aogf1HpiUBtxrWNens6c8FaV1130HIHq1l5628q4K3kdljNAb5QsHwP8DcZ1VQ29uzbgxDjx44/aPnMuplUqMLBalamDhusEXETcJOkd0fEj4apprLRuq+V2rG1VIypOGh55ZhKZtXNYlPLpkOsaWajWX9TAe+LiO8C8yV9rPfzEfGFPlazxJ59e/5oGiBvZt1MXmh9YZgrMrPh0N9UQP4zrI8JGoTWzkMfTjVjwgyea35umCsys+HQ31TA15K/Pz085ZSXPfv2MGPCjD6fmzF+Bg9sfmCYKzKz4VDsRVj+r6SJksZKukdSo6T3ZV3caHe4qYAZE2bQuLfRF7w2K0PFHse6JCJagLeTu1bAAuATWRVVDnqih7Z9bdRVHTpYe6KHxr2Nw1yZmWWt2GDNTxm8DfhBROzOqJ6y0bavjSAOO8cKeAeWWRkq9pTWn0p6CmgH/qekBqAju7JGv/xZV4cbsYKD1awcFXvZwOXAq4HFEdEFtAHnZVnYaNe6rxWACdUesZodaQbynVcnkjuetXCd76RcT9k4cDrrIUasR004CnCwmpWjYi8beDNwHPAIkN+NHThYD6m/qYDasbVMrJ7oYDUrQ8WOWBcDiyIisiymnOSD9XDXW50xYYaD1awMFXtUwONA30e6W5/27ttLTWXNH10noNBR449ysJqVoWJHrNOAJyQ9CHTmF0bEOzOpqgzs7d5L7djDX1lxxoQZPLr90WGqyMyGS7HBek2WRZSjvV3FBeudG+4cporMbLgUFawRcZ+ko4GFEXG3pFrg0J9xjb1de6kZW3PYNtPHT2d352727d9HVUXVMFVmZlkr9loBHwR+CHwtWTQb+M+MaioLxYxYG2obAGhs82mtZuWk2J1XHwVeA7QARMTTwPSsiioH7V3t/Qbr9PG5H6GvF2BWXooN1s6I2Jd/kJwk4EOvDmNvV+6ogMPJB+uLbS8OR0lmNkyKDdb7JP0NuS8VfAvwA+An2ZU1unXt76Jzf+cffddVbw5Ws/JUbLAuBxqBx4APASuBv82qqNFud2fu4l/F7LwCB6tZuSn2Iiw95HZWfSQiLoiIrxdzFpakpZLWSVovaXkfz1dLujV5/gFJ85PlUyX9QlKrpC/3WueXSZ+PJLcRN9fb1N4E0O8c68TqiVRVVDlYzcrMYYNVOddI2gGsA9Yl3x5wdX8dS6oAbgDOARYBF0ta1KvZFUBTRCwArgeuS5Z3AFcBf3WI7i+JiNOS24hLpeaOZqD/YJXE9PHTHaxmZaa/Eetfkjsa4IyImBIRU4CzgNdI+st+1j0TWB8RzyQ7vm7hjy81eB5wU3L/h8CbJSki2iLi14zSa742dRQ3YgUcrGZlqL9gvRS4OCKezS+IiGeA9wGX9bPubGBTwePNybI+20REN7AbmNp/2fxrMg1wlSQV0X5YFTtiBQerWTnqL1jHRsSO3gsjohEYm01J/bokIk4B/iS5XdpXI0nLJK2WtLqxcXiPE83PsfZ3uBU4WM3KUX/Bum+QzwFsAeYWPJ6TLOuzTXJsbD2w83CdRsSW5O89wPfITTn01e7GiFgcEYsbGhr6KTVd+RHr+KrDH24FML02F6y+IqNZ+egvWF8uqaWP2x7glH7WXQUslHSMpCrgImBFrzYrgMuT+xcA9x7uaANJlZKmJffHkvvW2Mf7qWPYNXc0U6EKxo7pf1DfML6B9u522rrahqEyMxsOh70IS0QM+kIrEdEt6UrgDnIXbPlWRKyVdC2wOiJWAN8Ebpa0HthFLnwBkLQRmAhUSTofWAI8B9yRhGoFcDfw9cHWmJWmjiZqx9ZSzPRv4bGsh7sotpmNHgP5zqsBi4iV5E4mKFx2dcH9DuDCQ6w7/xDdnp5WfVlp7mguascVHBysx04+NsuyzGyYFHvmlQ1AfsRaDJ99ZVZ+HKwZaO5o7vd01rwDV7jypQPNyoaDNQNN7cWPWPPXZPWI1ax8OFgzMJA51pqxNdRV1TlYzcqIgzVlEZGbY60sLlghOUlgr4PVrFw4WFO2t2sv3T3dRY9YwWdfmZUbB2vK8mddFbvzChysZuXGwZqy/JWt+vv2gEINtQ0OVrMy4mBN2WBHrI1tjfRET0ZVmdlwcrCmrNhvDyg0ffx09sf+A+ua2ejmYE3ZQK7Fmuezr8zKi4M1ZQP59oA8B6tZeXGwpuzAHGsRF7nOc7CalRcHa8qaO5qZUDWBijHFX3HRwWpWXjK9bOCRqKmjicnjJh+2zY1rbjzo8f6e/Qjxs/U/Y2zFWJadvizLEs0sYx6xpqy5o5lJ4yYNaJ2KMRWMrxpP677WbIoys2HlYE1ZU3sTk2sOP2LtS11VHXs692RQkZkNNwdrygYzYgWoq66jZV9L+gWZ2bBzsKasqaNpcMHqEatZ2XCwpqy5o7nfnVd9qauqY88+B6tZOXCwpmh/z35aOlsGPRWQv+SgmY1uDtYU7e7cDTC4EWt1HYCPDDArAw7WFOXPuhrsHCvgeVazMuBgTVH+6lSDOdxqYvVEAM+zmpUBB2uK0hixtnT6kCuz0c7BmqL8la2GMsfqEavZ6OdgTdFQRqw1lTVUqMJzrGZlwMGaoqEEqyTqqn0sq1k5cLCmqKm9iQpVMKFqwqDWr6uq8xyrWRlwsKYof50ASYNaf2L1RAerWRlwsKaoqWNwV7bKqx9XT0uHg9VstMs0WCUtlbRO0npJy/t4vlrSrcnzD0ianyyfKukXklolfbnXOqdLeixZ50sa7PAwA4O9slVefXU9Lfta2N+zP72izGzYZRaskiqAG4BzgEXAxZIW9Wp2BdAUEQuA64HrkuUdwFXAX/XR9VeBDwILk9vS9KsfnGK+PeBw6qvr6YkeduzdkWJVZjbcshyxngmsj4hnImIfcAtwXq825wE3Jfd/CLxZkiKiLSJ+TS5gD5A0E5gYEfdHRADfAc7P8DUMyJBHrOPqAdjWui2lisysFLIM1tnApoLHm5NlfbaJiG5gNzC1nz4399MnAJKWSVotaXVjY+MASx+cpvbBXYs1r746CdY9Dlaz0axsd15FxI0RsTgiFjc0NAzLNgd7LdY8j1jNykOWwboFmFvweE6yrM82kiqBemBnP33O6afPkujo7qBzf+eQRqz5C7G80PpCSlWZWSlkGayrgIWSjpFUBVwErOjVZgVweXL/AuDeZO60TxGxDWiR9KrkaIDLgB+nX/rADeXKVnlVFVXUVNZ4KsBslKvMquOI6JZ0JXAHUAF8KyLWSroWWB0RK4BvAjdLWg/sIhe+AEjaCEwEqiSdDyyJiCeAjwDfBmqAnyW3khvK6ayF6sfVeyrAbJTLLFgBImIlsLLXsqsL7ncAFx5i3fmHWL4aODm9KtMxlCtbFaqvdrCajXZlu/NquKU2Yq2u91SA2SjnYE1Jfo41ramAw0w1m9kI52BNSX4qYErNlCH1M2ncJDq6Ow70Z2ajj4M1JbvadwFDOyoAXpqj3bR7Uz8tzWykcrCmZFf7LiZWT6RyzND2B+aDeXPL5n5amtlI5WBNyVAvwJJ3YMTa4hGr2WjlYE3JrvZdQ55fhdzOqwpVeMRqNoo5WFOSVrCO0Rhm1c3yiNVsFHOwpqSpfWjfHlBozsQ5HrGajWIO1pTsat/FlHFDH7ECzK2f66MCzEYxB2sKIiK1qQCAuRPnsrlls08SMBulHKwpaOtqo6unK9WpgPbu9gPHxprZ6OJgTUH+dNY0R6zgY1nNRisHawryI8vUgrU+F6zP7X4ulf7MbHg5WFOQdrAeO/lYAJ5tejaV/sxseDlYU5DWtVjzptZMpa6qjmeankmlPzMbXg7WFKQ9YpXEsZOP5ZlmB6vZaORgTUHawQq56QCPWM1GJwdrCna172LsmLHUjq1Nrc98sPpYVrPRx8GagvzprLkvjk3HsZOPpaO7w1+FbTYKOVhTsKN9B9Nqp6XaZ/7IAE8HmI0+DtYUNLY10lDbkGqfDlaz0cvBmoLGvY00jE83WI+uPxohB6vZKORgTUEWI9bqymrmT5rPup3rUu3XzLLnYB2i/T372dW+K/VgBXhZw8t4ovGJ1Ps1s2w5WIdoZ/tOgkh95xXAommLeGrHU+zv2Z9632aWHQfrEDW2NQKkPscKsKhhEZ37O3m22dcMMBtNHKxD1Lg3CdYMpgIWNSwC8HSA2SjjYB2iHXt3ANmMWE+cdiLgYDUbbRysQ3RgKiCDEWv9uHpm1812sJqNMpkGq6SlktZJWi9peR/PV0u6NXn+AUnzC577ZLJ8naS3FizfKOkxSY9IWp1l/cXITwVksfMKctMBaxvXZtK3mWUjs2CVVAHcAJwDLAIulrSoV7MrgKaIWABcD1yXrLsIuAg4CVgKfCXpL++NEXFaRCzOqv5iNbY1MmncJMZWjM2k/1fMeAWPbX+Mzu7OTPo3s/RlOWI9E1gfEc9ExD7gFuC8Xm3OA25K7v8QeLNyVzI5D7glIjoj4llgfdLfiNO4tzGz0SrAGbPPoKuni99v/31m2zCzdGUZrLOBTQWPNyfL+mwTEd3AbmBqP+sGcKekNZKWZVD3gDTuTf+sq0JnzDoDgFVbVmW2DTNLV2WpCxiE10bEFknTgbskPRURv+rdKAndZQDz5s3LrJjGtkaOmXxMZv3Pq59HQ20Dq7Y6WM1GiyxHrFuAuQWP5yTL+mwjqRKoB3Yebt2IyP/9InAbh5giiIgbI2JxRCxuaMhuRJn1iFUSZ84+08FqNopkGayrgIWSjpFURW5n1IpebVYAlyf3LwDujdwl81cAFyVHDRwDLAQelDReUh2ApPHAEuDxDF/DYXX3dLO9dTuz6mZlup0zZp3Bk41P0tLZkul2zCwdmQVrMmd6JXAH8CTw/YhYK+laSe9Mmn0TmCppPfAxYHmy7lrg+8ATwM+Bj0bEfuAo4NeSfg88CNweET/P6jX0Z3vrdoLIPFj/5Og/IQju23hfptsxs3RkOscaESuBlb2WXV1wvwO48BDrfg74XK9lzwAvT7/SwdmyJzezMbuu9z65dL1m7muoqazhrmfu4h0nvCPTbZnZ0PnMqyHYumcrQOYj1urKal4///XcueHOTLdjZulwsA7BcAUrwJJjl7Bu5zqe3/185tsys6FxsA7BlpYtVKiC6eOnZ76tJcctAeD2P9ye+bbMbGgcrEOwtXUrMybMoGJMRf+Nh2hRwyIWNSziu499N/NtmdnQOFiHYEvLFmZPzHbHVZ4kLj31Un676bds2LVhWLZpZoPjYB2CrXu2Dsv8at4lp1yCEN/5/XeGbZtmNnAO1iHYumcrsyYMX7DOrZ/LOQvP4Surv0LrvtZh266ZDYyDdZDau9pp6mgatqmAvKtedxU79u7gq6u+OqzbNbPiOVgHaTgPtSr0qjmvYslxS7juN9exvXX7sG7bzIrjYB2k/DenzqvP7spZh3L9W6+ndV8rH/rph8hdWsHMRpLReNnAESG/Z37BlAXDvu1FDYv47Js+yyfu+gTX3nctf/eGvwPgxjU3Hna9ZaeX/PK1ZkcEB+sgrd+1nuqKauZMnJN63/0FJMCEqgmcPedsrrnvGlZtXcXbj387Y+QPIGYjgYN1kDY0beCYyceULMzGaAyXnnopALc/fTsbmjZw8ckXM2PCjJLUY2Yv8RBnkDY0bSjJNEChijEVXP7yy7nklEt4rvk5rr3vWn705I/o6O4oaV1mRzqPWAchItiwawNvnP/GUpeCJF539Os4bcZp3PbUbdy54U4e3Pwg7z35vbxy5itLXZ7ZEckj1kHY3radtq62ko9YC02snsjlL7+c5a9ZzsRxE/namq9x0+9vomt/V6lLMzviOFgHYf2u9QAcN/m4Elfyx46ZfAzLX7Occxeey283/ZYvPvBF2rvaS12W2RHFwToIpTzUqhgVYyo474Tz+MArP8AzTc/wpQe/5HlXs2HkYB2EtY1rqaqoYv6k+aUu5bDOmHUGy165jI3NG/nGQ99gf8/+UpdkdkRwsA7Cmm1rOGX6KYytGFvqUvr1ipmv4D0nvYfHXnyMT937qVKXY3ZEcLAOUETw0LaHOH3m6aUupWhvOPoNvG7e67juN9fx3Ud9oWyzrPlwqwF6tvlZmjuaOX3W6AlWSVx08kWMGTOGD6z4ACdMPYEzZp9R6rLMypZHrAP00LaHAEbdMaIVYyr4wYU/YGbdTM6/9Xy27dlW6pLMypaDdYDWbF1D5ZhKTpl+SqlLGbBptdP48UU/ZnfHbv70+3/qIwXMMuJgHaAHtjzAKdNPobqyutSlDMqpR53KTeffxP2b7+ey2y6ju6e71CWZlR0H6wC0dLbwX8//14Gvoh6t3r3o3Xx+yef5wRM/4NLbLqWzu7PUJZmVFe+8GoA71t9Bd083bz/+7aUuZcg+dvbH6NrfxfJ7lrOlZQs3v+tmjp50dKnLMisLHrEOwE/+8BOm1Ezh7Dlnl7qUVPz1a/+a7/3p93j4hYc5+asnc80vr+HFthdLXZbZqKcj4as9Fi9eHKtXrx5SH53dncz+wmzOWXgON7/r5n7bF3Ox6pFix94drN66mtueuo0xGsNr572W8044jzNnn8nLj3o5ddV1pS7RbESStCYiFvde7qmAIn37kW+zs30nl7/88lKXkrpptdNYumAppx51Kqu3rubhFx7m43d+HAAhpo+fzuy62UypncKUcVOYUjOF+nH11I6tpXZsLX9+1p9TVVFV4ldhNnJ4xFqE7p5ujv/n42kY38D9V9yPpH7XGU0j1r40dzTz/O7n2bR7E8/vfp5trdvY1b6Lrp6+L0NYVVF1IGhrx9Yyfux4ptVO48OLP8yr57562L/N1mw4lGTEKmkp8E9ABfCNiPjHXs9XA98BTgd2Au+NiI3Jc58ErgD2A38eEXcU02cWPnPfZ3i2+Vm+uPSLRYVqOZg0bhKTxk3i1KNOPbAsImjramPn3p20dLawt2vvgVtbVxvt3e3s3Zd7vGPvDtY2ruWeZ+8B4OTpJ/O2hW/j3IXncvacs1O/zkJEsLdrL+3d7XR2dzK+ajx1VXVUjKlIdTtmxcgsWCVVADcAbwE2A6skrYiIJwqaXQE0RcQCSRcB1wHvlbQIuAg4CZgF3C3p+GSd/vpMTUTwL6v/hWt/dS3vP+39vOP4d2SxmVFDEhOqJjChakJR7bt7ulk8azH3bbyPn2/4OZ//3ee57jfXUV9dz5LjlnDuwnNZumDpgL6na3/PfjY2b+QL93+BbXu2sa11Gy/seYFtrdvo3P/Hh41NqJpAfXU9k2smM3ncZCbXTGb82PH0RA/7Yz97u/bSuq/1wO3F1hfp2N+BEHXVdUysnsiM8TOYUz+HeRPncdXrryr69duRK7OpAElnA9dExFuTx58EiIh/KGhzR9Lmd5IqgReABmB5Ydt8u2S1w/bZl4FMBezbv4+ndjzFqi2r+M6j3+FXz/2KJcctYcVFKwZ0UsBonwpIS+FXbrd0tnD3M3ez8umVrHx6Jdtac6fVntRwEosaFnHitBOZPn46E6snUl1RTVtXG3s697C5ZTPPNj/L+l3rWbdz3UFnjE2qnsTMupnMnDCTSeMmUVVRRcWYCvbt30d7dzvtXe10dHfQ1tV2YHQ9rnIcFapgjMZQO7b2wC+LCVUT2NyymerKanqih9Z9rTR3NLNtzzbautoObPOYScdwylGncHLDybys4WXMqpt1YPs1Y2uoqayhqqJq1H+6KcyGIA67fCBti1keBB3dHezu2E1LZwu7O3dzy+O3HPRLMH/Lvx9mTJhBXXUdU2pe2hcwtXYq02qnHbhNrZlK7dhaqiqqqK6spqqiakhfCFqKqYDZwKaCx5uBsw7VJiK6Je0GpibL7++17uzkfn99Dsm3H/k2H/rph4Dcf6B/Puef+cgZH/FXSw9SX79gzpx9JmfMOoOz5pzF7X+4nfu33M9D2x7iR0/+iJ7o+aP21RXVzJ80n2MnH8t/O/a/sahhEX/Y+QdmTJhB7djazF9DRNDU0cSmlk3MGD+Dxxsf5/EXH+f2P9zO/uj7GrdCB6YhhJCEyAXtoe4f2F6GYdVf29GiuqL6wC/DcZXjkER1ZTU79+7k6Z1Ps6t9F80dzUW9tsoxlVSOqeTVc1/NPZfdk0p9ZXtUgKRlQH641Cpp3UD7eJZn+V/Jn0GYBuwYzIolMKJr7aSTdcmfn/GzEV1rXhB00z0qak2Mqlo76dzRSSc72Xlg4VM8NajOupM/93IvunzAnzL6PKsmy2DdAswteDwnWdZXm83JVEA9uZ1Yh1u3vz4BiIgbgZJ9Hpe0uq+PCCORa82Ga83GaKg1y8+3q4CFko6RVEVuZ9SKXm1WAPkDQy8A7o3cZ5QVwEWSqiUdAywEHiyyTzOzkspsxJrMmV4J3EHu0KhvRcRaSdcCqyNiBfBN4GZJ64Fd5IKSpN33gSeAbuCjEbnJrL76zOo1mJkNxhFxgkApSFqWTEeMeK41G641G6OhVgermVnKfAyRmVnKHKwpk7RU0jpJ6yUtL3U9AJK+JelFSY8XLJsi6S5JTyd/T06WS9KXkvoflTRsX+4laa6kX0h6QtJaSX8xgmsdJ+lBSb9Pav10svwYSQ8kNd2a7GQl2RF7a7L8AUnzh6vWgporJD0s6aejoNaNkh6T9Iik1cmyEfc+OBQHa4r00mm85wCLgIuVOz231L4NLO21bDlwT0QsBO5JHkOu9oXJbRnw1WGqEXI7Kj8eEYuAVwEfTX5+I7HWTuBNEfFy4DRgqaRXkTst+/qIWAA0kTttGwpO3wauT9oNt78Anix4PJJrBXhjRJxWcGjVSHwf9C0ifEvpBpwN3FHw+JPAJ0tdV1LLfODxgsfrgJnJ/ZnAuuT+14CL+2pXgpp/TO66ECO6VqAWeIjcWYA7gMre7wdyR7KcndyvTNppGGucQy6M3gT8FNBIrTXZ7kZgWq9lI/p9UHjziDVdfZ3GO/sQbUvtqIjIfwf2C8BRyf0R8RqSj5+vAB5ghNaafLR+BHgRuAvYADRHRP4bGgvrOej0bSB/+vZw+SLwf4D8OcNTGbm1AgRwp6Q1yVmUMELfB30p21NarXgREZJGzOEhkiYAPwL+d0S0qOBiJiOp1sgdW32apEnAbcCJpa2ob5LeDrwYEWskvaHE5RTrtRGxRdJ04C5JB52vOpLeB33xiDVdxZzGO1JslzQTIPk7/2VXJX0NksaSC9V/i4j/GMm15kVEM/ALch+nJyl3enbveg7UqoNP3x4OrwHeKWkjcAu56YB/GqG1AhARW5K/XyT3S+tMRvj7oJCDNV2j6ZTbwtOJLyc3n5lfflmyp/VVwO6Cj1+ZUm5o+k3gyYj4wgivtSEZqSKphtxc8JPkAvaCQ9Ta1+nbmYuIT0bEnIiYT+49eW9EXDISawWQNF5SXf4+sAR4nBH4PjikUk7wluMNOBf4A7n5tk+Vup6kpn8HtgFd5OafriA3Z3YP8DRwNzAlaStyRzZsAB4DFg9jna8lN7f2KPBIcjt3hNZ6KvBwUuvjwNXJ8mPJXddiPfADoDpZPi55vD55/tgSvRfeAPx0JNea1PX75LY2//9oJL4PDnXzmVdmZinzVICZWcocrGZmKXOwmpmlzMFqZpYyB6uZWcp85pUdMSTtJ3c4Tt75EbGxROVYGfPhVnbEkNQaERMGuI7I/T/54+/lNjsETwXYEUvSBEn3SHooufbnecny+cpdU/c75A7+nyvpE5JWJdf7/HRpK7eRzlMBdiSpSa5GBfAscCHwrshd6GUacL+k/CnIC4HLI+J+SUuSx2eSO8tnhaTXRcSvhrl+GyUcrHYkaY+I0/IPkgu+/L2k15G7nN5sXroU3XMRcX9yf0lyezh5PIFc0DpYrU8OVjuSXQI0AKdHRFdy9adxyXNtBe0E/ENEfG2Y67NRynOsdiSrJ3ed0i5JbwSOPkS7O4D/kVwnFkmzk+uEmvXJI1Y7kv0b8BNJjwGrgaf6ahQRd0p6GfC75KLbrcD7eOl6oGYH8eFWZmYp81SAmVnKHKxmZilzsJqZpczBamaWMgermVnKHKxmZilzsJqZpczBamaWsv8HE+4J1f6R4oUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(load_data, \"Fare\", \"Fare Distribution Passengers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "59f36781",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:52.561633Z",
     "iopub.status.busy": "2022-10-14T05:53:52.560633Z",
     "iopub.status.idle": "2022-10-14T05:53:52.567296Z",
     "shell.execute_reply": "2022-10-14T05:53:52.566341Z"
    },
    "papermill": {
     "duration": 0.035044,
     "end_time": "2022-10-14T05:53:52.569163",
     "exception": false,
     "start_time": "2022-10-14T05:53:52.534119",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_quartiles(dataset, feature, title, categories):\n",
    "    fig, axarr = plt.subplots(figsize=(5,5))\n",
    "    fare_ranges = pd.qcut(dataset[feature], len(categories), labels = categories) #. [0, .25, .5, .75, 1.]\n",
    "    axarr.set_title(title)\n",
    "    sns.barplot(x=fare_ranges, y=dataset.Survived, ci=None, ax=axarr).set_ylabel('Survival rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "158a4c02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:52.623067Z",
     "iopub.status.busy": "2022-10-14T05:53:52.621872Z",
     "iopub.status.idle": "2022-10-14T05:53:52.775201Z",
     "shell.execute_reply": "2022-10-14T05:53:52.774039Z"
    },
    "papermill": {
     "duration": 0.181608,
     "end_time": "2022-10-14T05:53:52.777124",
     "exception": false,
     "start_time": "2022-10-14T05:53:52.595516",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['cheap','Standard','Expensive','Luxury']\n",
    "\n",
    "plot_quartiles(load_data, \"Fare\", \"Survival Rate by Fare Ranges/Categories\", categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "38b21b48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:52.828283Z",
     "iopub.status.busy": "2022-10-14T05:53:52.827946Z",
     "iopub.status.idle": "2022-10-14T05:53:56.432742Z",
     "shell.execute_reply": "2022-10-14T05:53:56.431545Z"
    },
    "papermill": {
     "duration": 3.632625,
     "end_time": "2022-10-14T05:53:56.434498",
     "exception": false,
     "start_time": "2022-10-14T05:53:52.801873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 45.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/seaborn/categorical.py:1296: UserWarning: 16.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_swarm_survivors(load_data, \"Fare\",\"Sex\",\"Survivor Swarmplot for Age and Gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f73b439",
   "metadata": {
    "papermill": {
     "duration": 0.025378,
     "end_time": "2022-10-14T05:53:56.485269",
     "exception": false,
     "start_time": "2022-10-14T05:53:56.459891",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "fares for free?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b5f7c144",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:56.539075Z",
     "iopub.status.busy": "2022-10-14T05:53:56.538468Z",
     "iopub.status.idle": "2022-10-14T05:53:56.557254Z",
     "shell.execute_reply": "2022-10-14T05:53:56.555869Z"
    },
    "papermill": {
     "duration": 0.048387,
     "end_time": "2022-10-14T05:53:56.559566",
     "exception": false,
     "start_time": "2022-10-14T05:53:56.511179",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>413</th>\n",
       "      <td>414</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cunningham, Mr. Alfred Fleming</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
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       "      <td>Campbell, Mr. William</td>\n",
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       "      <td>Frost, Mr. Anthony Wood \"Archie\"</td>\n",
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       "      <td>807</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Mr. Thomas Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fry, Mr. Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B102</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>823</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Reuchlin, Jonkheer. John George</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                              Name   Sex  \\\n",
       "179          180         0       3               Leonard, Mr. Lionel  male   \n",
       "263          264         0       1             Harrison, Mr. William  male   \n",
       "271          272         1       3      Tornquist, Mr. William Henry  male   \n",
       "277          278         0       2       Parkes, Mr. Francis \"Frank\"  male   \n",
       "302          303         0       3   Johnson, Mr. William Cahoone Jr  male   \n",
       "413          414         0       2    Cunningham, Mr. Alfred Fleming  male   \n",
       "466          467         0       2             Campbell, Mr. William  male   \n",
       "481          482         0       2  Frost, Mr. Anthony Wood \"Archie\"  male   \n",
       "597          598         0       3               Johnson, Mr. Alfred  male   \n",
       "633          634         0       1     Parr, Mr. William Henry Marsh  male   \n",
       "674          675         0       2        Watson, Mr. Ennis Hastings  male   \n",
       "732          733         0       2              Knight, Mr. Robert J  male   \n",
       "806          807         0       1            Andrews, Mr. Thomas Jr  male   \n",
       "815          816         0       1                  Fry, Mr. Richard  male   \n",
       "822          823         0       1   Reuchlin, Jonkheer. John George  male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
       "179  36.0      0      0    LINE   0.0   NaN        S  \n",
       "263  40.0      0      0  112059   0.0   B94        S  \n",
       "271  25.0      0      0    LINE   0.0   NaN        S  \n",
       "277   NaN      0      0  239853   0.0   NaN        S  \n",
       "302  19.0      0      0    LINE   0.0   NaN        S  \n",
       "413   NaN      0      0  239853   0.0   NaN        S  \n",
       "466   NaN      0      0  239853   0.0   NaN        S  \n",
       "481   NaN      0      0  239854   0.0   NaN        S  \n",
       "597  49.0      0      0    LINE   0.0   NaN        S  \n",
       "633   NaN      0      0  112052   0.0   NaN        S  \n",
       "674   NaN      0      0  239856   0.0   NaN        S  \n",
       "732   NaN      0      0  239855   0.0   NaN        S  \n",
       "806  39.0      0      0  112050   0.0   A36        S  \n",
       "815   NaN      0      0  112058   0.0  B102        S  \n",
       "822  38.0      0      0   19972   0.0   NaN        S  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data.loc[load_data.Fare==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1798d960",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:56.615703Z",
     "iopub.status.busy": "2022-10-14T05:53:56.614626Z",
     "iopub.status.idle": "2022-10-14T05:53:56.621494Z",
     "shell.execute_reply": "2022-10-14T05:53:56.620294Z"
    },
    "papermill": {
     "duration": 0.036818,
     "end_time": "2022-10-14T05:53:56.623680",
     "exception": false,
     "start_time": "2022-10-14T05:53:56.586862",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def show_countplot(dataset, feature, title, fsize = (5,5)):\n",
    "    fig, ax = plt.subplots(figsize=fsize)\n",
    "    sns.countplot(dataset[feature], ax=ax).set_title(title)\n",
    "\n",
    "def show_compare_countplot(dataset, feature1, feature2, title):\n",
    "    fig, ax = plt.subplots(figsize=(5,5))\n",
    "    p = sns.countplot(x = feature1, hue = feature2, data = dataset, ax=ax).set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0f73bad4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:56.678988Z",
     "iopub.status.busy": "2022-10-14T05:53:56.678630Z",
     "iopub.status.idle": "2022-10-14T05:53:56.823447Z",
     "shell.execute_reply": "2022-10-14T05:53:56.822497Z"
    },
    "papermill": {
     "duration": 0.175255,
     "end_time": "2022-10-14T05:53:56.826066",
     "exception": false,
     "start_time": "2022-10-14T05:53:56.650811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_stacked(load_data,'Embarked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "362019e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:56.881168Z",
     "iopub.status.busy": "2022-10-14T05:53:56.880820Z",
     "iopub.status.idle": "2022-10-14T05:53:57.015279Z",
     "shell.execute_reply": "2022-10-14T05:53:57.014556Z"
    },
    "papermill": {
     "duration": 0.165284,
     "end_time": "2022-10-14T05:53:57.018127",
     "exception": false,
     "start_time": "2022-10-14T05:53:56.852843",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_countplot(load_data, \"Embarked\", 'Passenger count by boarding point')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fab35296",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:57.080158Z",
     "iopub.status.busy": "2022-10-14T05:53:57.079262Z",
     "iopub.status.idle": "2022-10-14T05:53:57.088948Z",
     "shell.execute_reply": "2022-10-14T05:53:57.087665Z"
    },
    "papermill": {
     "duration": 0.040538,
     "end_time": "2022-10-14T05:53:57.091606",
     "exception": false,
     "start_time": "2022-10-14T05:53:57.051068",
     "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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Embarked\n",
       "S       644\n",
       "C       168\n",
       "Q        77"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data['Embarked'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0119d111",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:57.148077Z",
     "iopub.status.busy": "2022-10-14T05:53:57.147689Z",
     "iopub.status.idle": "2022-10-14T05:53:57.315694Z",
     "shell.execute_reply": "2022-10-14T05:53:57.314079Z"
    },
    "papermill": {
     "duration": 0.199265,
     "end_time": "2022-10-14T05:53:57.318278",
     "exception": false,
     "start_time": "2022-10-14T05:53:57.119013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_compare_countplot(load_data, \"Embarked\", \"Survived\", \"Survivor count by place of embarktion\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cc2224dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:57.374871Z",
     "iopub.status.busy": "2022-10-14T05:53:57.374496Z",
     "iopub.status.idle": "2022-10-14T05:53:57.395344Z",
     "shell.execute_reply": "2022-10-14T05:53:57.394365Z"
    },
    "papermill": {
     "duration": 0.051719,
     "end_time": "2022-10-14T05:53:57.397326",
     "exception": false,
     "start_time": "2022-10-14T05:53:57.345607",
     "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>Embarked</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75</td>\n",
       "      <td>47</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93</td>\n",
       "      <td>30</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Embarked   C   Q    S\n",
       "Survived             \n",
       "0         75  47  427\n",
       "1         93  30  217"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(load_data, index = 'Survived', columns = 'Embarked', values = 'Ticket', aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3d9fb20d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:57.453462Z",
     "iopub.status.busy": "2022-10-14T05:53:57.453120Z",
     "iopub.status.idle": "2022-10-14T05:53:57.641939Z",
     "shell.execute_reply": "2022-10-14T05:53:57.640779Z"
    },
    "papermill": {
     "duration": 0.21917,
     "end_time": "2022-10-14T05:53:57.643813",
     "exception": false,
     "start_time": "2022-10-14T05:53:57.424643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_compare_countplot(load_data, \"Embarked\", \"Pclass\", \"Passenger count by place of embarktion and class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3fdfb429",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:57.700524Z",
     "iopub.status.busy": "2022-10-14T05:53:57.700161Z",
     "iopub.status.idle": "2022-10-14T05:53:57.715272Z",
     "shell.execute_reply": "2022-10-14T05:53:57.713973Z"
    },
    "papermill": {
     "duration": 0.046786,
     "end_time": "2022-10-14T05:53:57.717814",
     "exception": false,
     "start_time": "2022-10-14T05:53:57.671028",
     "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></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">C</th>\n",
       "      <th>1</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Q</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">S</th>\n",
       "      <th>1</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Survived\n",
       "Embarked Pclass          \n",
       "C        1             59\n",
       "         2              9\n",
       "         3             25\n",
       "Q        1              1\n",
       "         2              2\n",
       "         3             27\n",
       "S        1             74\n",
       "         2             76\n",
       "         3             67"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data.groupby(['Embarked', 'Pclass']).Survived.sum().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5ffa6b5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:57.775703Z",
     "iopub.status.busy": "2022-10-14T05:53:57.775348Z",
     "iopub.status.idle": "2022-10-14T05:53:57.934429Z",
     "shell.execute_reply": "2022-10-14T05:53:57.933426Z"
    },
    "papermill": {
     "duration": 0.190543,
     "end_time": "2022-10-14T05:53:57.936593",
     "exception": false,
     "start_time": "2022-10-14T05:53:57.746050",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_compare_countplot(load_data, \"Embarked\", \"Sex\", \"Passenger count by place of embarktion and class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "919c402b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:57.994968Z",
     "iopub.status.busy": "2022-10-14T05:53:57.994319Z",
     "iopub.status.idle": "2022-10-14T05:53:58.148402Z",
     "shell.execute_reply": "2022-10-14T05:53:58.147378Z"
    },
    "papermill": {
     "duration": 0.186508,
     "end_time": "2022-10-14T05:53:58.151038",
     "exception": false,
     "start_time": "2022-10-14T05:53:57.964530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV4AAAFNCAYAAABIaQcvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAfEUlEQVR4nO3debgcZZn+8e+dBAhLAIGokASCEtGAiBKRGVGUZQygyfwUFBwXRhzUCwSVZWDQiDi4iysuUVEWWSLKGDUKKJuySaJhCTESIJIElMMWEkAk+Pz+qPeESqe7T+ecU2/36XN/rutcp2vpqqe6q+9+663uakUEZmaWz4h2F2BmNtw4eM3MMnPwmpll5uA1M8vMwWtmlpmD18wss64LXknfkvSxQVjODyT972DUNBgkLZG0f8XrWCDpden2aZLOb6UeSf8j6btV1mbNSTpC0u/auP6QtFNV8/ezpu0lrZI0sqLlr3m9rK8swStpb0nXS1oh6WFJ10l6ZRXrioj3R8Qnq1j2QOQIzoGKiF0i4up+3O9TEfHeCkrqk6RFkl7UjnVbayRdLWnQ9w9Jr0nBukrS4ynMe4dXAUTEZhHxTBV19Pf1AjBqsIpoRNLmwM+BDwCzgA2B1wBP9WNZAhQR/xzUIgdI0qiIWN3uOoYbSS8ERkbEn9tdy3DRSft6RPwW2AxA0kTgHmDLTqmvmRwt3hcBRMSFEfFMRDwZEZdHxK2w7iGtpInpnWtUGr5a0hmSrgOeAE6UNLe8AkkfljQ73V7TRSBpoaQ3luYbJalH0ivS8I8k/TW1xK+VtEsrG5QO666T9CVJDwGnSXqhpCslPSTpQUk/lLRlmv88YHvgZ+nd+KQ0fq90JPCopFtaOGx5paQ7JD0i6fuSRqfl3C7pTaX6Nkg1vLxO7dtI+nla58OSfitpRJpW2yofLeliSSsl/UHSyxo8Hmuew9Lz925J96Y6Ti3Nu7Gkc9I2LJR0kqRlpen/LWl5WuciSfs1eTwOBuY0qOkISXen5dwj6T9K46+T9PX0vP+pvA5J20manR6bxZL+qzRtre4nSa9rpXZJIySdLOmutH/MkrRVmjZa0vlp/KOSbpb0vAbb1LuMlWk/+H/rztKv7dpT0g1p/fenZWxYmh6SjpZ0J3BnGndimvc+Se9p9ARJOoOiofX1tO9/vTR5f0l3pvWeJUml+70n7R+PSLpM0g6N1tFk3WuypFEdkr4iaamkxyTNk/Sa0v1PS8/VuekxXyBpSml6ubttpIout97nZ56kCQ2Li4hK/4DNgYeAc4ADgefUTD8NOL80PBEIYFQavhq4F9iFooW+BbASmFS6z83AYen2D4D/TbdnAD8szXcwsLA0/B5gDLAR8GVgfmnamuXU2aYjgNXAB1NNGwM7AQekZY0FrgW+XLrPEmD/0vC49LgcRPEGeEAaHttgnUuA24EJwFbAdaXtPAm4uDTvdOC2Bsv5NPAtYIP09xqKo4i1akzPy9PAIWm+EyhaFBs0mPf8mufvO+lxeRnF0c1L0vTPANcAzwHGA7cCy9K0nYGlwHalZb2wyb71K+ANdcZvCjwG7JyGtwV2qXnuPpy2623ACmCrNP1a4BvAaGB3oAfYt94+AbyuldqB44Ab0/ZuBHwbuDBNex/wM2ATYCSwB7B5g+09FNiOYn95G/A4sO0gbNcewF4U+/JEYCHwodJ6A7iCYr/bGJgK/A3YNT3WF6R5dmpQ99XAe2vGBcWR8JYUjZIeYGpp/10MvCTV9FHg+j5yZiKl3GiSJbV1vAPYOq3neOCvwOjSfv13itfoSIrXzo31XtPAicBtaT8QxX6/dcN6BztoGzwoL6HYaZelnWM28LzaF22TB+v0muWdD8xItydRBPEmtS8OijAsT/th7/3q1LhlWu8W9V5kNfMeAdzbxzb/O/DHek9SGv5v4Lya+1wGvLvB8pYA7y8NHwTclW5vl7Zz8zR8CXBSg+WcDvyUOi8S1g3T8k42ArgfeE2DeWuDd3zpvr/n2TfGuymFJfBeng2vnYAHgP1JAd/k8d2E4o1qozrTNgUeBd4CbFznubuP9GZTqu+dFG9qzwBjStM+Dfyg3j7B2sHbsHaKINuvNLwtxZvaKIo3/+uB3frxupoPTB/odtVZ7oeAS0vDQQrpNHw28JnS8IvoX/DuXRqeBZycbv8SOLJm33sC2KHJY9G7361X8NZZziPAy0r79a9L0yYDTzZ4vSzqfS5a+ctyci0iFkbEERExnuJdcjuKFmarltYMXwAcnm6/Hfi/iHiiznoXU+z0b5K0CTAt3bf30OAz6dDgMYoHEWCb/tQk6XmSLkqHmo9RvDk0W9YOwKHpMOtRSY8Ce1O8KFtZ518oHkci4j6KFvBbVHRvHEjxJlPP5ylaE5enQ/GTW1lfFP3qy3rX2YK/lm4/QeqLS/cvb0d5HYspXvSnAQ+kx7PR+vajaAWtc64gIh6naPG9H7hf0i8kvbg0y/JIr5ak97HcDng4IlbWTBvXaCNbrH0H4NLS87yQIgifB5xH8YZ7UTps/5ykDeqtQ9K7JM0vLWdX1t7H+rVdkl6kovvpr2nf/RTr7rvl56z2OfxLo8elD432kR2Ar5S282GKVmSfz8P6knRC6tJYkda1BWtve22No5W6QWtMAO5qdb3ZP04WEX+iaDnsmkY9TtF66fX8enerGb4CGCtpd4oAvqDJKi9M80wH7kgvECgCezpFC2ULindHKJ7gVtTW9Kk07qURsTnFIYyazL+UosW7Zelv04j4TJN1lvuMtqdo4fQ6J63zUOCGiFhet+iIlRFxfES8gOKN6CNq3I+6Zn0q+oHH16yzP+5Py1lnHam+CyJib4oXXwCfbbCcg2jQv5uWc1lEHEDxRvYniq6PXuPK/Yk8+1jeB2wlaUzNtN7Hsum+2qT2pcCBNc/16IhYHhFPR8QnImIy8K/AG4F31W5P6uP8DnAMxSHslhRdT+Xt6O92fTM9RpPSvvs/rPs6KO+/97PuvthM7b7fl6XA+2oer40j4vr1XE7TOlJ/7knAWym6QLek6J5pNQNqa35hqzNXHrySXizpeEnj0/AEiiC8Mc0yH3itis/cbQGc0tcyI+Jp4EcUrbetKIK4kYuAf6P4VEU5oMdQ9D0+RPFi+tR6bFY9Y4BVwApJ4yj6fMr+BrygNHw+RUv8Dan1PVrFyZrxNHa0pPEqTsycClxcmvZ/wCso+hPPbbQASW+UtFN6ga6gaHk1+pTIHpLenN7hP0TxeN3YYN5WzQJOkfSc9DgdU6ptZ0n7StqIom/tySa1HQj8ot6EdPQxXdKmqeZVNct5LnCsipOQh1J0hc2JiKUUh/2fTs/HbsCRFM8VFPvqQZK2kvR8isekldq/BZzRe4JI0lhJ09Pt10t6qYrPmj5G0QVRb5s3pQiOnnS//+TZxstAt2tMWveqdGTwgXqPa8ks4AhJk9OR5Mf7mL923+/Ltyj2kV0AJG2RtmegausYQ9H12QOMkjSD4pxUf3wX+KSkSSrsJmnrRjPnaPGuBF4F3CTpcYoX7u0UHdlExBUUAXIrMI+iw70VF1C0Vn8UTT4+EhH3AzdQtCbKQXUuxSHScuAOBh4on6AIvhUUgfCTmumfBj6aDp9OSC+G6RStix6Kd8wTaf6cXABcTtFPehew5gx7RDwJ/BjYsc66yyYBv6YIoxuAb0TEVQ3m/SnFIfsjFH2Fb05vegNxOkWXxT2pjkt49qOFG1GcfHuQ4hDvudR5I5a0K7AqIu5tsI4RwEcoWnoPA/uwdpjcRPE4PAicARwSEQ+laYdTHP3cB1wKfDwifp2mnQfcQtEtdTlr70/Nav8KxXmNyyWtpNjXXpWmPT89Bo9RdEFck9azloi4A/gixXP2N+ClFN1LZf3drhMojgBXUrSqy9u1joj4JUVX4ZUU3VZXNpufYvsPUfEJha/2MS8RcSnF0cJFqevjdoo32oGqreMyihO0f6bIgr+zbrdmq86keEO6nOK5/B7Fici6es9mWxdI79gvioh3tLuWVkn6AMWJt33W4z4nAdtExEn9WN8RFCdY9l7f+5oNlq77yvBwlbofjgRmtruWZiRtK+nVKj7bujPFkc+l67mYJcD3B704s0wcvF1AxYfhlwK/jIhr211PHzak+BzrSopD1J9SfL60ZRExKyIWVlCbWRbuajAzy8wtXjOzzBy8ZmaZVX51ssG2zTbbxMSJE9tdhpl1mXnz5j0YEWNzrGvIBe/EiROZO3du3zOama0HSf396vN6c1eDmVlmDl4zs8wcvGZmmTl4zcwyc/CamWXm4DUzy8zBa2aWmYPXzCwzB6+ZWWYOXjOzzBy8ZmaZDblrNQw1957+0rauf/sZt7V1/Wa2Lrd4zcwyc/CamWXm4DUzy8zBa2aWmYPXzCwzB6+ZWWYOXjOzzBy8ZmaZOXjNzDJz8JqZZebgNTPLzMFrZpZZpcEraaqkRZIWSzq5zvQjJPVImp/+3ltlPWZmnaCyq5NJGgmcBRwALANuljQ7Iu6omfXiiDimqjrMzDpNlS3ePYHFEXF3RPwDuAiYXuH6zMyGhCqDdxywtDS8LI2r9RZJt0q6RNKECusxM+sI7T659jNgYkTsBlwBnFNvJklHSZoraW5PT0/WAs3MBluVwbscKLdgx6dxa0TEQxHxVBr8LrBHvQVFxMyImBIRU8aOHVtJsWZmuVQZvDcDkyTtKGlD4DBgdnkGSduWBqcBCyusx8ysI1T2qYaIWC3pGOAyYCRwdkQskHQ6MDciZgPHSpoGrAYeBo6oqh4zs05R6Y9dRsQcYE7NuBml26cAp1RZg5lZp2n3yTUzs2HHwWtmlpmD18wsMwevmVlmDl4zs8wcvGZmmTl4zcwyc/CamWXm4DUzy8zBa2aWmYPXzCwzB6+ZWWYOXjOzzCq9Opl1vld/7dVtXf91H7yures3awe3eM3MMnPwmpll5uA1M8vMwWtmlpmD18wsMwevmVlmDl4zs8wcvGZmmTl4zcwyc/CamWXm4DUzy8zBa2aWmYPXzCwzB6+ZWWYOXjOzzBy8ZmaZOXjNzDJz8JqZZebgNTPLzMFrZpaZg9fMLDMHr5lZZg5eM7PMHLxmZpk5eM3MMnPwmpllVmnwSpoqaZGkxZJObjLfWySFpClV1mNm1gkqC15JI4GzgAOBycDhkibXmW8McBxwU1W1mJl1kipbvHsCiyPi7oj4B3ARML3OfJ8EPgv8vcJazMw6RpXBOw5YWhpelsatIekVwISI+EWFdZiZdZS2nVyTNAI4Ezi+hXmPkjRX0tyenp7qizMzq1CVwbscmFAaHp/G9RoD7ApcLWkJsBcwu94JtoiYGRFTImLK2LFjKyzZzKx6VQbvzcAkSTtK2hA4DJjdOzEiVkTENhExMSImAjcC0yJiboU1mZm1XWXBGxGrgWOAy4CFwKyIWCDpdEnTqlqvmVmnG1XlwiNiDjCnZtyMBvO+rspazMw6hb+5ZmaWmYPXzCwzB6+ZWWYOXjOzzBy8ZmaZOXjNzDJz8JqZZebgNTPLzMFrZpaZg9fMLDMHr5lZZg5eM7PMHLxmZpk5eM3MMnPwmpll5uA1M8vMwWtmlpmD18wsMwevmVlmDl4zs8wcvGZmmbX0K8OSNga2j4hFFdez3vY48dy2rn/e59/V1vWb2dDTZ4tX0puA+cCv0vDukmZXXJeZWddqpavhNGBP4FGAiJgP7FhZRWZmXa6V4H06IlbUjIsqijEzGw5a6eNdIOntwEhJk4BjgeurLcvMrHu10uL9ILAL8BRwAbACOK7KoszMulkrLd6DI+JU4NTeEZIOBX5UWVVmZl2slRbvKS2OMzOzFjRs8Uo6EDgIGCfpq6VJmwOrqy7MzKxbNetquA+YC0wD5pXGrwQ+XGVRZmbdrGHwRsQtwC2SLoiIpzPWZGbW1Vo5uTZR0qeBycDo3pER8YLKqjIz62KtnFz7PvBNin7d1wPnAudXWZSZWTdrJXg3jojfAIqIv0TEacDB1ZZlZta9WulqeErSCOBOSccAy4HNqi3LzKx7tdLiPQ7YhOKrwnsA7wDeXWVRZmbdrGmLV9JI4G0RcQKwCvjPLFWZmXWxpi3eiHgG2DtTLWZmw0IrXQ1/lDRb0jslvbn3r5WFS5oqaZGkxZJOrjP9/ZJukzRf0u8kTV7vLTAzG2JaObk2GngI2Lc0LoCfNLtT6qY4CzgAWAbcLGl2RNxRmu2CiPhWmn8acCYwtfXyzcyGnj6DNyL626+7J7A4Iu4GkHQRMB1YE7wR8Vhp/k3xBdbNbBho6ccu+2kcsLQ0vAx4Ve1Mko4GPgJsyNqtajOzrtT2n3ePiLMi4oXAfwMfrTePpKMkzZU0t6enJ2+BZmaDrMrgXQ5MKA2PT+MauQj493oTImJmREyJiCljx44dvArNzNqg2fV4P9LsjhFxZh/LvhmYJGlHisA9DHh7zTomRcSdafBg4E7MzLpcsz7eMQNZcESsTl8xvgwYCZwdEQsknQ7MjYjZwDGS9geeBh7B34gzs2Gg2fV4PzHQhUfEHGBOzbgZpdv+0UwzG3b6/FSDpNHAkRS/NFy+Hu97KqzLzKxrtXJy7Tzg+cAbgGsoTpKtrLIoM7Nu1krw7hQRHwMej4hzKE6CrfN5XDMza00rwdv7e2uPStoV2AJ4bnUlmZl1t1a+uTZT0nOAjwGzKS6C/rFKqzIz62KtBO/30+UhrwH8A5dmZgPUSlfDPZJmStpPkiqvyMysy7USvC8Gfg0cDSyR9HVJvji6mVk/9Rm8EfFERMyKiDcDuwObU3Q7mJlZP7R0kRxJ+0j6BjCP4ksUb620KjOzLtbKN9eWAH8EZgEnRsTjVRdlZtbNWvlUw241vxRhZmYD0OyykCdFxOeAMySt85M8EXFspZWZmXWpZi3ehen/3ByFmJkNF80uC/mzdPO2iPhDpnrMzLpeK59q+KKkhZI+ma7VYGZmA9DK53hfD7we6AG+Lek2SXV/lNLMzPrW0ud4I+KvEfFV4P3AfGBG83uYmVkjfQavpJdIOk3SbcDXgOspLoZuZmb90MrneM+m+On1N0TEfRXXY7aWa167T1vXv8+1/na8Db6mwStpJHBPRHwlUz1mZl2vaVdDug7vBEkbZqrHzKzrtdLVcA9wnaTZwJrrNETEmZVVZWbWxVoJ3rvS3whgTLXlmJl1vz6DNyI+kaMQM7PhopXLQl4F1LtIzr6VVGRm1uVa6Wo4oXR7NPAWYHU15ZiZdb9Wuhrm1Yy6TtLvK6rHzKzrtdLVsFVpcASwB7BFZRWZmXW5Vroa5lH08Yqii+Ee4MgqizIz62atdDXsmKMQM7PhouE31yS9UtLzS8PvkvRTSV+t6X4wM7P10Owrw98G/gEg6bXAZ4BzgRXAzOpLMzPrTs26GkZGxMPp9tuAmRHxY+DHkuZXXpmZWZdq1uIdKak3mPcDrixNa+WknJmZ1dEsQC8ErpH0IPAk8FsASTtRdDeYmVk/NPuV4TMk/QbYFrg8Inq/NjwC+GCO4szMulHTLoOIuLHOuD9XV46ZWfdr6ccuzcxs8FQavJKmSlokabGkk+tM/4ikOyTdKuk3knaosh4zs05QWfCm32s7CzgQmAwcLmlyzWx/BKZExG7AJcDnqqrHzKxTVNni3RNYHBF3R8Q/KH6peHp5hoi4KiKeSIM34p+NN7NhoMrgHQcsLQ0vS+MaORL4ZYX1mJl1hI74IoSkdwBTgH0aTD8KOApg++23z1iZmdngq7LFuxyYUBoen8atRdL+wKnAtIh4qt6CImJmREyJiCljx46tpFgzs1yqDN6bgUmSdpS0IXAYMLs8g6SXU1yMZ1pEPFBhLWZmHaOy4I2I1cAxwGXAQmBWRCyQdLqkaWm2zwObAT+SNF/S7AaLMzPrGpX28UbEHGBOzbgZpdv7V7l+M7NO5G+umZll5uA1M8vMwWtmlpmD18wsMwevmVlmDl4zs8wcvGZmmTl4zcwyc/CamWXm4DUzy8zBa2aWWUdcj9dsKPr68T9r6/qP+eKb2rp+6z+3eM3MMnPwmpll5uA1M8vMwWtmlpmD18wsMwevmVlmDl4zs8wcvGZmmTl4zcwyc/CamWXm4DUzy8zBa2aWmYPXzCwzB6+ZWWYOXjOzzBy8ZmaZOXjNzDJz8JqZZebgNTPLzMFrZpaZg9fMLDMHr5lZZg5eM7PMHLxmZpk5eM3MMnPwmpll5uA1M8vMwWtmllmlwStpqqRFkhZLOrnO9NdK+oOk1ZIOqbIWM7NOUVnwShoJnAUcCEwGDpc0uWa2e4EjgAuqqsPMrNOMqnDZewKLI+JuAEkXAdOBO3pniIglado/K6zDzKyjVNnVMA5YWhpelsatN0lHSZoraW5PT8+gFGdm1i5D4uRaRMyMiCkRMWXs2LHtLsfMbECqDN7lwITS8Pg0zsxsWKsyeG8GJknaUdKGwGHA7ArXZ2Y2JFQWvBGxGjgGuAxYCMyKiAWSTpc0DUDSKyUtAw4Fvi1pQVX1mJl1iio/1UBEzAHm1IybUbp9M0UXhJnZsDEkTq6ZmXUTB6+ZWWYOXjOzzBy8ZmaZOXjNzDJz8JqZZebgNTPLzMFrZpaZg9fMLDMHr5lZZg5eM7PMHLxmZpk5eM3MMnPwmpll5uA1M8vMwWtmlpmD18wsMwevmVlmDl4zs8wcvGZmmTl4zcwyc/CamWXm4DUzy8zBa2aWmYPXzCwzB6+ZWWYOXjOzzBy8ZmaZOXjNzDJz8JqZZebgNTPLzMFrZpaZg9fMLDMHr5lZZg5eM7PMHLxmZpk5eM3MMnPwmpll5uA1M8us0uCVNFXSIkmLJZ1cZ/pGki5O02+SNLHKeszMOkFlwStpJHAWcCAwGThc0uSa2Y4EHomInYAvAZ+tqh4zs05RZYt3T2BxRNwdEf8ALgKm18wzHTgn3b4E2E+SKqzJzKztqgzeccDS0vCyNK7uPBGxGlgBbF1hTWZmbTeq3QW0QtJRwFFpcJWkRYO4+G2AB/t7Z33h3YNYSl0Dqo+PV34AMbDH79jOro9qD8AGVNsHzxzESuob2GNXvcGub4dBXFZTVQbvcmBCaXh8GldvnmWSRgFbAA/VLigiZgIzqyhS0tyImFLFsgeD6xuYTq6vk2sD11elKrsabgYmSdpR0obAYcDsmnlmA71NxkOAKyMiKqzJzKztKmvxRsRqSccAlwEjgbMjYoGk04G5ETEb+B5wnqTFwMMU4Wxm1tUq7eONiDnAnJpxM0q3/w4cWmUNLaikC2MQub6B6eT6Ork2cH2VkY/szczy8leGzcwyG9bB29dXmttJ0tmSHpB0e7trqSVpgqSrJN0haYGk49pdU5mk0ZJ+L+mWVN8n2l1TPZJGSvqjpJ+3u5ZakpZIuk3SfElz211PLUkfTs/t7ZIulDS63TWtj2EbvC1+pbmdfgBMbXcRDawGjo+IycBewNEd9tg9BewbES8DdgemStqrvSXVdRywsN1FNPH6iNi90z6yJWkccCwwJSJ2pTh5P6ROzA/b4KW1rzS3TURcS/FJj44TEfdHxB/S7ZUU4VH7rcS2icKqNLhB+uuokxmSxgMHA99tdy1D1Chg4/T5/02A+9pcz3oZzsHbylearQ/pinIvB25qcylrSYfx84EHgCsioqPqA74MnAT8s811NBLA5ZLmpW+OdoyIWA58AbgXuB9YERGXt7eq9TOcg9cGSNJmwI+BD0XEY+2upywinomI3Sm+MbmnpF3bXNIakt4IPBAR89pdSxN7R8QrKLrijpb02nYX1EvScyiOTncEtgM2lfSO9la1foZz8LbylWZrQNIGFKH7w4j4SbvraSQiHgWuorP6y18NTJO0hKKLa19J57e3pLWlViUR8QBwKUXXXKfYH7gnInoi4mngJ8C/trmm9TKcg7eVrzRbHenSnd8DFkZE9ZdqWU+SxkraMt3eGDgA+FNbiyqJiFMiYnxETKTY766MiI5psUnaVNKY3tvAvwGd9Omae4G9JG2S9sX96OyTlOsYtsGbLkPZ+5XmhcCsiFjQ3qqeJelC4AZgZ0nLJB3Z7ppKXg28k6KlNj/9HdTuokq2Ba6SdCvFG+wVEdFxH9nqYM8DfifpFuD3wC8i4ldtrmmN1F9/CfAH4DaKHBtS32LzN9fMzDIbti1eM7N2cfCamWXm4DUzy8zBa2aWmYPXzCwzB68NGZJOTVekujV9hO1Vkr7be4EeSasa3G8vSTel+yyUdFrWws1qDIlfGTaT9C/AG4FXRMRTkrYBNoyI97Zw93OAt0bELemqdDtXWatZX9zitaFiW+DBiHgKICIejIj7JF0tac1lCyV9KbWKfyNpbBr9XIqLqfRew+GONO9pks6TdIOkOyX9V+ZtsmHKwWtDxeXABEl/lvQNSfvUmWdTih9S3QW4Bvh4Gv8lYJGkSyW9r+ai2bsB+wL/AsyQtF2F22AGOHhtiEjX190DOAroAS6WdETNbP8ELk63zwf2Tvc9HZhCEd5vB8pff/1pRDwZEQ9SXEynky4GY13Kfbw2ZETEM8DVwNWSbgPe3dddSve9C/impO8APZK2rp2nwbDZoHOL14YESTtLmlQatTvwl5rZRgCHpNtvB36X7ntwuooVwCTgGeDRNDw9/Ubb1sDrKC6qY1Ypt3htqNgM+Fq63ONqYDFFt8MlpXkep7jo+UcpfnnibWn8O4EvSXoi3fc/IuKZlMW3UnQxbAN8MiKG1E/I2NDkq5PZsJU+z7sqIr7Q7lpseHFXg5lZZm7xmpll5havmVlmDl4zs8wcvGZmmTl4zcwyc/CamWXm4DUzy+z/A2gfZsL5gd7/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_compare(load_data, \"SibSp\", title = \"Survival rate by siblings / spouses aboard the Titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ce662461",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:58.211465Z",
     "iopub.status.busy": "2022-10-14T05:53:58.211117Z",
     "iopub.status.idle": "2022-10-14T05:53:58.383829Z",
     "shell.execute_reply": "2022-10-14T05:53:58.382232Z"
    },
    "papermill": {
     "duration": 0.204905,
     "end_time": "2022-10-14T05:53:58.386038",
     "exception": false,
     "start_time": "2022-10-14T05:53:58.181133",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_countplot(load_data, \"SibSp\", \"SibSp Distribution\", (15,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "af6206f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:58.444210Z",
     "iopub.status.busy": "2022-10-14T05:53:58.443840Z",
     "iopub.status.idle": "2022-10-14T05:53:58.913165Z",
     "shell.execute_reply": "2022-10-14T05:53:58.911848Z"
    },
    "papermill": {
     "duration": 0.500336,
     "end_time": "2022-10-14T05:53:58.915206",
     "exception": false,
     "start_time": "2022-10-14T05:53:58.414870",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_compare_countplot(load_data, \"SibSp\", \"Survived\", \"Survivor count by number of siblings / spouses aboard the Titanic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3d81d0ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:58.973804Z",
     "iopub.status.busy": "2022-10-14T05:53:58.973378Z",
     "iopub.status.idle": "2022-10-14T05:53:59.139827Z",
     "shell.execute_reply": "2022-10-14T05:53:59.138897Z"
    },
    "papermill": {
     "duration": 0.198424,
     "end_time": "2022-10-14T05:53:59.141948",
     "exception": false,
     "start_time": "2022-10-14T05:53:58.943524",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_countplot(load_data, \"Parch\", 'Parch Disstribution', (15,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "49089515",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:59.202777Z",
     "iopub.status.busy": "2022-10-14T05:53:59.202385Z",
     "iopub.status.idle": "2022-10-14T05:53:59.352557Z",
     "shell.execute_reply": "2022-10-14T05:53:59.351390Z"
    },
    "papermill": {
     "duration": 0.183375,
     "end_time": "2022-10-14T05:53:59.354896",
     "exception": false,
     "start_time": "2022-10-14T05:53:59.171521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_compare(load_data, \"Parch\", title=\"Survival rate by Parch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ee5e9485",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:59.428135Z",
     "iopub.status.busy": "2022-10-14T05:53:59.427140Z",
     "iopub.status.idle": "2022-10-14T05:53:59.634948Z",
     "shell.execute_reply": "2022-10-14T05:53:59.634029Z"
    },
    "papermill": {
     "duration": 0.245632,
     "end_time": "2022-10-14T05:53:59.636833",
     "exception": false,
     "start_time": "2022-10-14T05:53:59.391201",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NzDIUFpqSBkp6VNLjkp6UdE2aPkrSUkmrJS2QtE+avm8aX53mNxRVm5nZ7iqypfkKcFJEHA1MAE6TdBzwr8DXImI08CJwaVr+UuDFNP1raTkzs1IpLDSjYksaHZA+AZwEfD9NnwOck4bPTuOk+SdLUlH1mZntjkKPaUrqJ2kZsB5YAjwDvBQRr6dFWoC6NFwHrAFI8zcDQ4usz8wsV6GhGRHbI2ICUA+8H3hXd7cpaYakJklNGzZs6O7mzMyy7JGz5xHxEvAgcDwwRFL/NKseWJuG1wIjAdL8A4FNHWzrtoiYGBEThw8fXnTpZmY7KPLs+XBJQ9LwfsBHgJVUwnNKWmw6cF8aXpTGSfN/EhFRVH1mZruj/64X2W0jgDmS+lEJ54URsVjSCmC+pC8BvwFmp+VnA3dKWg28AJxfYG1mZrulsNCMiCeA93Yw/fdUjm+2n74NOLeoeszMeoLvCDIzy+DQNDPL4NA0M8vg0DQzy+DQNDPL4NA0M8vg0DQzy+DQNDPL4NA0M8vg0DQzy+DQNDPL4NA0M8vg0DQzy+DQNDPL4NA0M8vg0DQzy+DQNDPL4NA0M8vg0DQzy+DQNDPL4NA0M8vg0DQzy9Cl0JT0QFemmZn1dTt977mkgcDbgWGSDgKUZh0A1BVcm5lZ6ew0NIH/DnwSOBRo5s3Q/DNwS3FlmZmV005DMyJuAm6S9E8RcfMeqsnMrLR21dIEICJulvRBoKF6nYi4o6C6zMxKqUuhKelO4G+BZcD2NDkAh6aZ7VW6FJrARGBMRESRxZiZlV1Xr9P8LfA3RRZiZtYbdLWlOQxYIelR4JXWiRHRWEhVZmYl1dXQnFVkEWZmvUVXz57/rOhCzMx6g66ePX+ZytlygH2AAcDWiDigqMLMzMqoqy3Nwa3DkgScDRxXVFG18sdrx3Vr/cO+uLyHKjGzssp+ylFU/Dtwas+XY2ZWbl3tnv991ejbqFy3ua2QiszMSqyrZ8/Pqhp+HXiOShfdzGyv0tVjmp8ouhAzs96gqw8hrpd0r6T16XOPpPqiizMzK5uungj6LrCIynM1DwX+I00zM9urdDU0h0fEdyPi9fS5HRheYF1mZqXU1dDcJOnjkvqlz8eBTUUWZmZWRl0NzUuA84DngXXAFODigmoyMyutrl5ydC0wPSJeBJB0MHADlTA1M9trdLWlOb41MAEi4gXgvcWUZGZWXl0NzbelV/gCbS3NrrZSzcz6jK4G378Bv5J0dxo/F7iumJLMzMqrq3cE3SGpCTgpTfr7iFhRXFlmZuXU5S52CkkHpZnt1bIfDddVkkZKelDSCklPSpqZph8saYmkp9PPg9J0Sfq6pNWSnpB0TFG1mZntrsJCk8rTkD4dEWOoPLD4CkljgM8BD0TEEcADaRzgdOCI9JkBfKPA2szMdkthoRkR6yLisTT8MrASqKPySLk5abE5wDlp+GzgjvSQ40eAIZJGFFWfmdnuKLKl2UZSA5XrOpcCh0TEujTreeCQNFwHrKlarSVNa7+tGZKaJDVt2LChuKLNzDpQeGhKGgTcA3wyIv5cPS8igjdf2NYlEXFbREyMiInDh/uZIWa2ZxUampIGUAnMuRHxgzT5T63d7vRzfZq+FhhZtXp9mmZmVhpFnj0XMBtYGRFfrZq1CJiehqcD91VNvyidRT8O2FzVjTczK4Uib4WcBEwDlktalqZdBVwPLJR0KfAHKk9PArgfOANYDfwF8Cs2zKx0CgvNiPgFoE5mn9zB8gFcUVQ9ZmY9YY+cPTcz6yscmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhkKC01J35G0XtJvq6YdLGmJpKfTz4PSdEn6uqTVkp6QdExRdZmZdUeRLc3bgdPaTfsc8EBEHAE8kMYBTgeOSJ8ZwDcKrMvMbLcVFpoR8XPghXaTzwbmpOE5wDlV0++IikeAIZJGFFWbmdnu2tPHNA+JiHVp+HngkDRcB6ypWq4lTTMzK5WanQiKiAAidz1JMyQ1SWrasGFDAZWZmXVuT4fmn1q73enn+jR9LTCyarn6NO0tIuK2iJgYEROHDx9eaLFmZu3t6dBcBExPw9OB+6qmX5TOoh8HbK7qxpuZlUb/ojYsaR4wGRgmqQW4GrgeWCjpUuAPwHlp8fuBM4DVwF+ATxRVl5lZdxQWmhFxQSezTu5g2QCuKKoWM7Oe4juCzMwyODTNzDI4NM3MMjg0zcwyODTNzDI4NM3MMjg0zcwyODTNzDI4NM3MMjg0zcwyODTNzDI4NM3MMjg0zcwyODTNzDI4NM3MMhT2PE3reX+8dly31j/si8t7qBKzvZdbmmZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJpmZhl8ydEe9L7P3NGt9e8d3EOFmNluc0vTzCyDQ9PMLIND08wsg0PTzCyDQ9PMLIND08wsg0PTzCyDr9O0Nt2/jvQr3Vrfj66z3sAtTTOzDA5NM7MMDk0zswwOTTOzDA5NM7MMDk0zswwOTTOzDA5NM7MMDk0zswwOTTOzDA5NM7MMDk0zswwOTTOzDA5NM7MMDk0zswwOTTOzDA5NM7MMDk0zswylCk1Jp0laJWm1pM/Vuh4zs/ZK844gSf2AW4GPAC3AryUtiogVta3MbNe6+36l5q9c1EOVdKzs9fUmpQlN4P3A6oj4PYCk+cDZgEPTesQfrx3XrfX94jeDcoVmHbCmarwF+ECNarES6v7bMnuokL1Q0X9welNLWBGxx3a2M5KmAKdFxH9L49OAD0TEle2WmwHMSKNHAat6sIxhwMYe3F5Pc33dU+b6ylwb7H31HR4RwzuaUaaW5lpgZNV4fZq2g4i4DbitiAIkNUXExCK23RNcX/eUub4y1waur1qZzp7/GjhC0ihJ+wDnA4tqXJOZ2Q5K09KMiNclXQn8P6Af8J2IeLLGZZmZ7aA0oQkQEfcD99ewhEK6/T3I9XVPmesrc23g+tqU5kSQmVlvUKZjmmZmpefQTMp8C6ek70haL+m3ta6lPUkjJT0oaYWkJyXNrHVN1SQNlPSopMdTfdfUuqaOSOon6TeSFte6lvYkPSdpuaRlkppqXU97koZI+r6kpyStlHR8oftz97ztFs7fUXULJ3BBWW7hlHQCsAW4IyLeU+t6qkkaAYyIiMckDQaagXNK9LsTsH9EbJE0APgFMDMiHqlxaTuQ9ClgInBARJxZ63qqSXoOmBgRpbxOU9Ic4KGI+Ha68ubtEfFSUftzS7Oi7RbOiHgVaL2FsxQi4ufAC7WuoyMRsS4iHkvDLwMrqdzdVQpRsSWNDkifUrUUJNUDHwW+XetaehtJBwInALMBIuLVIgMTHJqtOrqFszT/4/cWkhqA9wJLa1zKDlLXdxmwHlgSEaWqD7gR+BfgjRrX0ZkAfiSpOd2RVyajgA3Ad9PhjW9L2r/IHTo0rUdIGgTcA3wyIv5c63qqRcT2iJhA5S6z90sqzSEOSWcC6yOiuda17MSHIuIY4HTginS4qCz6A8cA34iI9wJbgULPSTg0K7p0C6d1LB0rvAeYGxE/qHU9nUndtgeB02pcSrVJQGM6bjgfOEnSXbUtaUcRsTb9XA/cS+VwVlm0AC1VvYfvUwnRwjg0K3wL525KJ1pmAysj4qu1rqc9ScMlDUnD+1E52fdUTYuqEhGfj4j6iGig8u/uJxHx8RqX1UbS/ukEH6nbewpQmqs4IuJ5YI2ko9Kkkyn4cZKluiOoVsp+C6ekecBkYJikFuDqiJhd26raTAKmAcvTcUOAq9LdXWUwApiTrpB4G7AwIkp3WU+JHQLcW/nbSH/gexHxw9qW9Bb/BMxNDZ7fA58ocme+5MjMLIO752ZmGRyaZmYZHJpmZhkcmmZmGRyaZmYZHJrWK0nanp6681tJd0t6eze311DGp0hZ+Tg0rbf6a0RMSE99ehX4x66sJMnXJlu3ODStL3gIGC3pLElL04MbfizpEABJsyTdKelh4E5Jh0i6Nz1j83FJH0zb6SfpW+m5mz9KdxCZ7cChab1aajmeDiyn8qzM49KDG+ZTeXJQqzHA30XEBcDXgZ9FxNFU7lNuvfvrCODWiBgLvAT8wx75EtaruKtivdV+VbdtPkTl/vejgAXpwcj7AM9WLb8oIv6ahk8CLoLKE5CAzZIOAp6NiNZtNgMNRX4B650cmtZb/TU97q2NpJuBr0bEIkmTgVlVs7d2YZuvVA1vB9w9t7dw99z6kgN585F+03ey3APAZdD2gOIDiy7M+g6HpvUls4C7JTUDO3ufzUzgREnLqXTDx+yB2qyP8FOOzMwyuKVpZpbBoWlmlsGhaWaWwaFpZpbBoWlmlsGhaWaWwaFpZpbBoWlmluH/A63QsOR5KB2YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_compare_countplot(load_data, \"Parch\", \"Survived\", \"Survivor count by Parch\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6a3fa4",
   "metadata": {
    "papermill": {
     "duration": 0.029209,
     "end_time": "2022-10-14T05:53:59.695496",
     "exception": false,
     "start_time": "2022-10-14T05:53:59.666287",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e510a4d8",
   "metadata": {
    "papermill": {
     "duration": 0.02862,
     "end_time": "2022-10-14T05:53:59.754030",
     "exception": false,
     "start_time": "2022-10-14T05:53:59.725410",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "creating a new feature with name column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ac881334",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:59.814759Z",
     "iopub.status.busy": "2022-10-14T05:53:59.813493Z",
     "iopub.status.idle": "2022-10-14T05:53:59.818801Z",
     "shell.execute_reply": "2022-10-14T05:53:59.817973Z"
    },
    "papermill": {
     "duration": 0.037596,
     "end_time": "2022-10-14T05:53:59.820714",
     "exception": false,
     "start_time": "2022-10-14T05:53:59.783118",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pd.unique(load_data['Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4327b3f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:59.882816Z",
     "iopub.status.busy": "2022-10-14T05:53:59.881562Z",
     "iopub.status.idle": "2022-10-14T05:53:59.889718Z",
     "shell.execute_reply": "2022-10-14T05:53:59.889075Z"
    },
    "papermill": {
     "duration": 0.041925,
     "end_time": "2022-10-14T05:53:59.891825",
     "exception": false,
     "start_time": "2022-10-14T05:53:59.849900",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "load_data['Title']=load_data['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())\n",
    "testds['Title']=testds['Name'].apply(lambda x: x.split(',')[1].split('.')[0].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "01f43b9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:53:59.952954Z",
     "iopub.status.busy": "2022-10-14T05:53:59.951961Z",
     "iopub.status.idle": "2022-10-14T05:54:00.169916Z",
     "shell.execute_reply": "2022-10-14T05:54:00.168778Z"
    },
    "papermill": {
     "duration": 0.250972,
     "end_time": "2022-10-14T05:54:00.172042",
     "exception": false,
     "start_time": "2022-10-14T05:53:59.921070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_countplot(load_data, \"Title\", 'Title Distribution', (15,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e3553612",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:00.238722Z",
     "iopub.status.busy": "2022-10-14T05:54:00.238378Z",
     "iopub.status.idle": "2022-10-14T05:54:00.244479Z",
     "shell.execute_reply": "2022-10-14T05:54:00.243262Z"
    },
    "papermill": {
     "duration": 0.040032,
     "end_time": "2022-10-14T05:54:00.246834",
     "exception": false,
     "start_time": "2022-10-14T05:54:00.206802",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# #merge in 'Mr'\n",
    "# load_data['Title'].replace(to_replace=['Don','Major','Sir','Col','Capt','Jonkheer'], value='Mr', inplace=True)\n",
    "# #merge in 'Mrs'\n",
    "# load_data['Title'].replace(to_replace=['Mme','Ms','Lady','Mlle','the Countess'], value='Mrs', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "586b8f8f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:00.308001Z",
     "iopub.status.busy": "2022-10-14T05:54:00.307433Z",
     "iopub.status.idle": "2022-10-14T05:54:00.529875Z",
     "shell.execute_reply": "2022-10-14T05:54:00.528752Z"
    },
    "papermill": {
     "duration": 0.255154,
     "end_time": "2022-10-14T05:54:00.531856",
     "exception": false,
     "start_time": "2022-10-14T05:54:00.276702",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_countplot(load_data,\"Title\", 'Title Distribution after substitution', (15,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6a32539f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:00.593029Z",
     "iopub.status.busy": "2022-10-14T05:54:00.592631Z",
     "iopub.status.idle": "2022-10-14T05:54:00.598670Z",
     "shell.execute_reply": "2022-10-14T05:54:00.597257Z"
    },
    "papermill": {
     "duration": 0.040119,
     "end_time": "2022-10-14T05:54:00.601720",
     "exception": false,
     "start_time": "2022-10-14T05:54:00.561601",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label classes: [0, 1]\n"
     ]
    }
   ],
   "source": [
    "label = \"Survived\"\n",
    "\n",
    "classes = load_data[label].unique().tolist()\n",
    "print(f\"Label classes: {classes}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cba83686",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:00.665112Z",
     "iopub.status.busy": "2022-10-14T05:54:00.664747Z",
     "iopub.status.idle": "2022-10-14T05:54:00.882791Z",
     "shell.execute_reply": "2022-10-14T05:54:00.881669Z"
    },
    "papermill": {
     "duration": 0.252637,
     "end_time": "2022-10-14T05:54:00.884913",
     "exception": false,
     "start_time": "2022-10-14T05:54:00.632276",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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E2CUinijcHjOzltAwXCX1BR6OiF91U3vMzFpCw2GBfB/XDSWt3k3tMTNrCc0MCzwMTJE0EVh6X4GIOLFYq8zMerlmwvWh/K8PsHbZ5piZtYZ2wzUiju2OhpiZtZJmbjk4Gah345aPFWmRmVkLaGZY4Iiqx2sAewKLyzTHzKw1NDMsMK1m1hRJdxRqj5lZS2hmWOAtVZN9gG2A/sVaZGbWApoZFphGGnMVaTjgYeDAko2y3mPEVaM7vMy1u19coCVmPUszwwJDu6MhZmatpM1vaEl6v6T1qqYPkHSVpJNqhgrMzKxGo6+/ngm8AiDpI8AJwHnAAmB8M5VL2lXSA5JmSTqyQbk9JYWk4c033cys52o0LNA3Ip7Nj/cBxkfEBGCCpOntVZxv+nIq8HFgLnCnpIkRMbOm3NrA14C/dqL9ZmY9UqOea19JlfDdCbip6rlm3gjbFpgVEbMj4hXSbQt3r1Pu+8CPgX83UaeZWa/QKFwvBv4s6SrgZeAWAEnvJA0NtGcDYE7V9Nw8bylJ7wM2jIg/dKTRZmY9XaNffz1e0o3A+sD1EVH5Cmwf4KsrumJJfYATgTFNlB0LjAUYPHjwiq7azKy4hpf3EXF7nXkPNln348CGVdOD8ryKtYEtgJslAawHTJQ0KiKm1qxzPPlNtOHDhy93nwMzs56mqR8o7KQ7gWGShuabbe8LTKw8GRELImJARAyJiCHA7cBywWpm1hsVC9eIWAwcAlwH3A9cFhEzJB0naVSp9ZqZ9QTNvOvfaRExCZhUM6/uz3JHxA4l22Jm1p1KDguYma2yHK5mZgU4XM3MCnC4mpkV4HA1MyvA4WpmVoDD1cysAIermVkBDlczswIcrmZmBThczcwKcLiamRXgcDUzK8DhamZWgMPVzKwAh6uZWQEOVzOzAhyuZmYFOFzNzApwuJqZFeBwNTMrwOFqZlaAw9XMrACHq5lZAQ5XM7MCHK5mZgU4XM3MCnC4mpkV4HA1MyvA4WpmVoDD1cysAIermVkBDlczswIcrmZmBThczcwKcLiamRXgcDUzK8DhamZWgMPVzKwAh6uZWQEOVzOzAhyuZmYFOFzNzAooGq6SdpX0gKRZko6s8/w3JM2UdI+kGyVtVLI9ZmbdpVi4SuoLnAqMADYDRkvarKbYXcDwiNgSuAL4San2mJl1p5I9122BWRExOyJeAS4Bdq8uEBGTI+KlPHk7MKhge8zMuk3JcN0AmFM1PTfPa8uBwLUF22Nm1m36rewGAEjaHxgOfLSN58cCYwEGDx7cjS0zM+uckj3Xx4ENq6YH5XnLkLQzcBQwKiIW1asoIsZHxPCIGD5w4MAijTUz60olw/VOYJikoZJWB/YFJlYXkLQ1cCYpWJ8q2BYzs25VLFwjYjFwCHAdcD9wWUTMkHScpFG52E+BtYDLJU2XNLGN6szMepWiY64RMQmYVDPvmKrHO5dcv5nZyuJvaJmZFeBwNTMrwOFqZlaAw9XMrACHq5lZAQ5XM7MCesTXXztj3ukXdHiZgf+zf4GWmJktzz1XM7MCHK5mZgU4XM3MCnC4mpkV4HA1MyvA4WpmVoDD1cysAIermVkBDlczswIcrmZmBThczcwKcLiamRXgcDUzK8DhamZWgMPVzKwAh6uZWQEOVzOzAhyuZmYFOFzNzApwuJqZFeBwNTMrwOFqZlaAw9XMrACHq5lZAQ5XM7MCHK5mZgU4XM3MCnC4mpkV4HA1MyvA4WpmVoDD1cysAIermVkBDlczswIcrmZmBThczcwKcLiamRVQNFwl7SrpAUmzJB1Z5/k3SLo0P/9XSUNKtsfMrLsUC1dJfYFTgRHAZsBoSZvVFDsQeC4i3gn8AvhxqfaYmXWnkj3XbYFZETE7Il4BLgF2rymzO3BufnwFsJMkFWyTmVm3KBmuGwBzqqbn5nl1y0TEYmAB8NaCbTIz6xaKiDIVS58Bdo2Ig/L054DtIuKQqjL35TJz8/RDuczTNXWNBcbmyU2BBxqsegDwdIPnm7GidbRCG1phG3pCG1phG3pCG7pjGzaKiIEruI6l+nVVRXU8DmxYNT0oz6tXZq6kfkB/4JnaiiJiPDC+mZVKmhoRwzvV4i6qoxXa0Arb0BPa0Arb0BPa0BO2oaNKDgvcCQyTNFTS6sC+wMSaMhOBz+fHnwFuilJdaTOzblSs5xoRiyUdAlwH9AXOjogZko4DpkbEROA3wPmSZgHPkgLYzKzXKzksQERMAibVzDum6vG/gb26eLVNDR8UrqMV2tAK29AT2tAK29AT2tATtqFDir2hZWa2KvPXX83MCujV4SopJF1QNd1P0jxJ16yM5SSNknRkZ+tvY53rSvpyzbzXJE2XNEPS3ZIOl9ShY1lVx32Srpa0bkfb1pk2deW+6Wi9K3Dch0s6qWp64Qq08xFJAxo8f1Teh/fk/bmdpLPqfLuxUr69c3KMpFPy43GSjmiw7vUkXSLpIUnTJE2StEl+bpnzUNK+kp5vtEwT++KAfP7dK2mupG9XPdepfVy7fyV9R9IQpY99dqteHa7Ai8AWktbM0x9n+Y97AemkK71cREyMiBM6Un8T1gW+XDPv5YjYKiI2z3WPAL5X1WY1EbaVOrYgvZn4lU62r6k2VenKfdPReju17oiYGhGHNtuQmnOmaZI+AHwKeF9EbAnsDMyJiIMiYmad8n3pov0pScDvgJsjYuOI2Ab4NvD2XGRdlj8PaWeZRusbAXwd+EREvAdYDPy7o+1uwncK1Am0f5x7e7hCesPsk/nxaODiyhP5L/X5kqYA53d2OUmbS7oDWBP4D9I9Eeott7SXAMwGHpB0N3BOLreWpDskPSjpRUkzJd0qadO8/Ob5+em55zIMOAHYOM/7aa57NUl3SrqHFIpjgUOVbpJzASksZ0q6S9KOVW27UtIfJf0DWL1qX9xG/vacpI1zmWmSbpH0Lkn9JT1aCWxJb5I0R9Jq9Q5IRDyV23RIDvoxkiZKuinvw/b2/bl53Y9K+rSkn+TezR8r65S0jaQ/53ZeB6idej+a171+3jdr1ymzraTb8n6rPi476PWe4FuANfLxuV3Slnn+RZKezj2uuZLenue/VdL1Sr3Rs3I7kXScpK9Xrft44CDg6YhYlPfj0xHxhKSbJQ3P5RZK+nk+rz6QF29zu9tSe5yBA4BXI+KMquN4N/B/+bybDmym1DNceh4C7wS+LulCScrLhKTnJL0k6QVJY/I6h0j6u6QLgQnAC8B8SYeSXldflDRZ0lbAmnkfP5hfJ/fmfTg8H48p+TX093zMrpT0R9J5/D1Jayp9KemNef9sKOnXSj3kBUpXV2dKGpb3w9O5vgclHSZpYH7NvZy34095GxplyrIiotf+AxYCW5LuS7BGPgF2AK7Jz48DpgFrrshywMnAZ/Ny7wOubGO5McApudw/gGtyuXtzuYdzPeuQXuhrknonE6rXkx+vnp8fAtxX1fZPAK+SXqR98jo+AjwPLAF+RfrYG8C7gMdyG8aQAr9/nl5C+gJHX+By0jflAG4EhuXH25E+ewxwFbBjfrwPcFbtPq1zfOaTejFjSF9/fkuT+/7/SC/c9wIvASPyc78D/is/dyswsKo9r7ZT79W5ri1zPW+qU2YdoF9+XH1cqsucDCzKjz8GTM+PTyCfM6SQ/HmefxJwTH78SSBI3xQaAvwtz+8DPAQMzm16EDgN+Gh+/mZgeH4cwN4dOJfHAKdU7dsj2jjODwK/qHMM9wRuAN4B3E86n9YnfWxyCfDr3P7bgO2rjs3QvPxBpHNTeZsD+BCpA3B+VXseAQbkx/fkY7Un6Zz9Fek8+jewS96+50lfne8DzCL11vsDj5LOtcofjIV5vYuBPUjnweXA/nkfzyCdUzfk/XATqZd+EenbXG/Ix+WBRplS71/Rj2J1h4i4R+lWhaOp+dhXNjEiXl7B5W4DjiKdOC+QdnZby1XcmMucDvwpz3uOdJmyOTCc9Nc6cr1L1yNpEHBlRPxDy9/H5hOkQLwrT68FDMuP5+S2nZy38e+SHgUqY2A3RsQCWHoZWDmR7gdukLQW8EHg8qr1viH/fykpxCaTXlinNdj2em6IiGclNbPvr42IVyXdm7f1j3n+vaQXyqbAFrnN5DJqp94pwEjSC/MdwN51yvQHzlW6Yqg+LtW2J71QiYibcs90HVIw9wfuIP1hfDiX/wjw6Vz+D5Key48fkfSMpK1JwXFXRDwmaRvgw8COwKVa/ladr5F6fUs1sT+X0cZxXqeN4tuTesJL8rr/DLw/P/cK6Y/qEknTScdmPunY3JHXQ94flaGCORExJa/3MtJV4M+q2tafdE4uyes+E9gnIv4laT7ptfO3/G9IXvdjpOO/QFLkbbkpIs6TVDlPHyb1arep2q4XgKHA90mvkWuAl0nBvXNu979I9zxZrWp76mZKrVYYFoD0Ta+fUf9y6MUVXS4iLgJG5clJpBd5W8tVljmYdCewfUgv5HWAJ3I9OwFbAYcCu5F6HNXreRmYJOljdaoW8Eqk8c2tIt2ucTLpxH+hwbYCLKp6/BrwJWCjXOdXSOfD/Kq6t4qId+fyE4Fd82XxNqRgbpOkd+R1PJVn1R6HRvu+clm8hHSpWvm84BLSZ7MFzKhq43t4fbyubr2RxsIXkXqWGwEn1ln394HJkcahlx6XJo0E/pLb8t9NLnsWqWf5BeDs3M7XIuLmiPgecAip91bt3xHxWp26Gu3PWssdZ9IV1TaNF1vOkqplXuP1Y/MU6fxYJyLWJP3Rr+yPyrGcQeoUdOSzoJHrh/wHrqod1fvkWeADWrZnsigvey7p2J9J6uA8nY/ZW4GDSaF9FmkfrUfq7U4gBWzlHGuUKUu1SrieDRwbEfeWWC4HxWzSpedVpEuYhstJ2pj0Yj2SFKpvI43/zM7Tt5Eu58bUriciTsrr2ZIUmGtXVX0dVX9FJb0nb0fl1o23kF4oKL1rO5gGN7qJiJdIIX846VLsYUl75eUl6b253ELSV5p/RbrkrPcCr2zHQOAM0uVoWy+ezh4z8vYMVHoDCKVx2Mq5XLfefDyWRMSPSb3YK+qsuz+vvxk0po1130L+8o2kHUgvzudJPfwFucznq8r/Bdgvlx8BvLnqud8Bu5J6gtdJ2jT3miu2Il3mNqPp/Znbu8xxJl8CK90kiTx/S1JPdB9SoPQn9cTvyEUW1y5D6u2/GeiTrz52Jv0xqxicj9uPSFdxd+f5C4GD8pXVc6TjeQupA/CXfE6txetffFq/wSY+QHrdnEp6zVaWuZH0NftKD7Qf8ISkL+b1XQmcRxr6ux44OiImA98ihW9luab0+mEBgEh31Tqp3YKdX25v4HOkXs8WwH4R8Ww7y/yU9JdZpIM6i3RC3Ec6ATcgXZJU329hb+Bzkl4FngR+mC+lpyh9lOTaiPhmfjHMy/+/lrdhPOlS5jTg9HxJvRgYExGL6gwvVO+Hu5TeHBtNCubTJR2d23kJr78ALiWNV+1Qp5o186Xhanm955N6CG2ts1PHLC/7itJd107Kl5H9yOHaoN6v5zbeQ+o1HVKnzE9IwwJHA3+oXW3+fxzwVUmv5HnzJX2DNC56gKSPkHptQ3P5Y4GLJc0gjUU+VrMdk0m9yNfyH8yTlT4Wt5h0zowljae2t086uj/rHec9gF9K+hapl/YIab+tRbo6Wpt0yXw4adyRyjKksdA9SGO+h+a6X871PFm13gdIV0nb5Pr3yiH/ZuAbknYBvgjcTtrXQRoK2wk4htTLP6iJ7TuZ9EfuQdKw0oCImJm390TSObMb8APgCNJ7JX1Iwf5F0rDbvZIOI72Gp0fE/Eavo1r+hpZZOyTtCYyKiM+3W7hj9fYhXYbuFRH/6Mq6e6I8LnxNHnZpVO69wK8jYttuaVghrTIsYFaEpFHA8aQxuq6sdzNSz/TGVSFYmyXpYNK48dEruy0ryj1XM7MC3HM1MyvA4WpmVoDD1cysAIer9Tr5W1HT878nJT2eHy+sfCNH6fvnH6xapuEdocy6Wkt8ztVWLRHxDOkD9kgaR/oK5s9qiu1A+mD6rd3ZNrMK91ytZeTe6jX585QHA4flHu2Ha8otd+evldJga2nuuVrLyTdFOYOqHq2knaqKjAcOzjfG2Y70rbZ693Ew6zSHq61S2rgj1BvaXsKscxyutqpZekeold0Qa20ec7VWVXs3MaD+HaEqd/4y60oOV2tVVwN71HtDi3RHqAOVfiplBrB7t7fOWp7vLWBmVoB7rmZmBThczcwKcLiamRXgcDUzK8DhamZWgMPVzKwAh6uZWQEOVzOzAv4frKQzU7onsbcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_compare(load_data, \"Title\", title=\"Survival rate by Title\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228a7846",
   "metadata": {
    "papermill": {
     "duration": 0.030414,
     "end_time": "2022-10-14T05:54:00.946449",
     "exception": false,
     "start_time": "2022-10-14T05:54:00.916035",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Feature Cabin and Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6dc2984c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:01.009531Z",
     "iopub.status.busy": "2022-10-14T05:54:01.009170Z",
     "iopub.status.idle": "2022-10-14T05:54:01.020982Z",
     "shell.execute_reply": "2022-10-14T05:54:01.020115Z"
    },
    "papermill": {
     "duration": 0.046667,
     "end_time": "2022-10-14T05:54:01.023664",
     "exception": false,
     "start_time": "2022-10-14T05:54:00.976997",
     "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>Cabin</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C85</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C123</td>\n",
       "      <td>113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>NaN</td>\n",
       "      <td>211536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>B42</td>\n",
       "      <td>112053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>W./C. 6607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>C148</td>\n",
       "      <td>111369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>NaN</td>\n",
       "      <td>370376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cabin            Ticket\n",
       "0     NaN         A/5 21171\n",
       "1     C85          PC 17599\n",
       "2     NaN  STON/O2. 3101282\n",
       "3    C123            113803\n",
       "4     NaN            373450\n",
       "..    ...               ...\n",
       "886   NaN            211536\n",
       "887   B42            112053\n",
       "888   NaN        W./C. 6607\n",
       "889  C148            111369\n",
       "890   NaN            370376\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data[['Cabin', 'Ticket']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "eb830584",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:01.087743Z",
     "iopub.status.busy": "2022-10-14T05:54:01.086700Z",
     "iopub.status.idle": "2022-10-14T05:54:01.094307Z",
     "shell.execute_reply": "2022-10-14T05:54:01.093623Z"
    },
    "papermill": {
     "duration": 0.041944,
     "end_time": "2022-10-14T05:54:01.096066",
     "exception": false,
     "start_time": "2022-10-14T05:54:01.054122",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Extract Leading Letter\n",
    "load_data['Ticket_2letter'] = load_data.Ticket.apply(lambda x: x[:2])\n",
    "testds['Ticket_2letter'] = testds.Ticket.apply(lambda x: x[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "88ef9528",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:01.159385Z",
     "iopub.status.busy": "2022-10-14T05:54:01.158339Z",
     "iopub.status.idle": "2022-10-14T05:54:01.165919Z",
     "shell.execute_reply": "2022-10-14T05:54:01.165116Z"
    },
    "papermill": {
     "duration": 0.040965,
     "end_time": "2022-10-14T05:54:01.167717",
     "exception": false,
     "start_time": "2022-10-14T05:54:01.126752",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Extract Ticket Length\n",
    "load_data['Ticket_len'] = load_data.Ticket.apply(lambda x: len(x))\n",
    "testds['Ticket_len'] = testds.Ticket.apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "39cd8d50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:01.231878Z",
     "iopub.status.busy": "2022-10-14T05:54:01.231502Z",
     "iopub.status.idle": "2022-10-14T05:54:01.240591Z",
     "shell.execute_reply": "2022-10-14T05:54:01.239034Z"
    },
    "papermill": {
     "duration": 0.043997,
     "end_time": "2022-10-14T05:54:01.242998",
     "exception": false,
     "start_time": "2022-10-14T05:54:01.199001",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Extract Number of Cabins\n",
    "load_data['Cabin_num'] = load_data.Ticket.apply(lambda x: len(x.split()))\n",
    "testds['Cabin_num'] = testds.Ticket.apply(lambda x: len(x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8934c50b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:01.306690Z",
     "iopub.status.busy": "2022-10-14T05:54:01.306357Z",
     "iopub.status.idle": "2022-10-14T05:54:01.314342Z",
     "shell.execute_reply": "2022-10-14T05:54:01.313197Z"
    },
    "papermill": {
     "duration": 0.042448,
     "end_time": "2022-10-14T05:54:01.316160",
     "exception": false,
     "start_time": "2022-10-14T05:54:01.273712",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Extract Leading Letter\n",
    "load_data['Cabin_1letter'] = load_data.Ticket.apply(lambda x: x[:1])\n",
    "testds['Cabin_1letter'] = testds.Ticket.apply(lambda x: x[:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b11d8f",
   "metadata": {
    "papermill": {
     "duration": 0.031385,
     "end_time": "2022-10-14T05:54:01.379004",
     "exception": false,
     "start_time": "2022-10-14T05:54:01.347619",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Family Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0c24ba26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:01.442470Z",
     "iopub.status.busy": "2022-10-14T05:54:01.441220Z",
     "iopub.status.idle": "2022-10-14T05:54:01.448951Z",
     "shell.execute_reply": "2022-10-14T05:54:01.447897Z"
    },
    "papermill": {
     "duration": 0.041206,
     "end_time": "2022-10-14T05:54:01.450839",
     "exception": false,
     "start_time": "2022-10-14T05:54:01.409633",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "load_data['Fam_size'] = load_data['SibSp'] + load_data['Parch'] + 1\n",
    "testds['Fam_size'] = testds['SibSp'] + testds['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "618e26a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:01.517647Z",
     "iopub.status.busy": "2022-10-14T05:54:01.517304Z",
     "iopub.status.idle": "2022-10-14T05:54:01.702771Z",
     "shell.execute_reply": "2022-10-14T05:54:01.701265Z"
    },
    "papermill": {
     "duration": 0.222281,
     "end_time": "2022-10-14T05:54:01.704822",
     "exception": false,
     "start_time": "2022-10-14T05:54:01.482541",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_compare(load_data, \"Fam_size\", title = \"Survival rate by family size\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b0d3d4d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:01.768792Z",
     "iopub.status.busy": "2022-10-14T05:54:01.768390Z",
     "iopub.status.idle": "2022-10-14T05:54:01.994521Z",
     "shell.execute_reply": "2022-10-14T05:54:01.993117Z"
    },
    "papermill": {
     "duration": 0.260017,
     "end_time": "2022-10-14T05:54:01.997030",
     "exception": false,
     "start_time": "2022-10-14T05:54:01.737013",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_compare_countplot(load_data, \"Fam_size\",\"Survived\", \"Survivor count by family size\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "684fd8b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:02.061087Z",
     "iopub.status.busy": "2022-10-14T05:54:02.060716Z",
     "iopub.status.idle": "2022-10-14T05:54:02.071248Z",
     "shell.execute_reply": "2022-10-14T05:54:02.069847Z"
    },
    "papermill": {
     "duration": 0.045172,
     "end_time": "2022-10-14T05:54:02.073702",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.028530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "load_data['Fam_type'] = pd.cut(load_data.Fam_size, [0,1,4,7,11], labels=['Solo', 'Small', 'Big', 'Very big'])\n",
    "testds['Fam_type'] = pd.cut(testds.Fam_size, [0,1,4,7,11], labels=['Solo', 'Small', 'Big', 'Very big'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1409ed71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:02.136635Z",
     "iopub.status.busy": "2022-10-14T05:54:02.136279Z",
     "iopub.status.idle": "2022-10-14T05:54:02.270410Z",
     "shell.execute_reply": "2022-10-14T05:54:02.269718Z"
    },
    "papermill": {
     "duration": 0.167777,
     "end_time": "2022-10-14T05:54:02.272283",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.104506",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_compare(load_data, \"Fam_type\", title = \"Survival rate by family type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6749f6ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:02.337251Z",
     "iopub.status.busy": "2022-10-14T05:54:02.335998Z",
     "iopub.status.idle": "2022-10-14T05:54:02.362351Z",
     "shell.execute_reply": "2022-10-14T05:54:02.361479Z"
    },
    "papermill": {
     "duration": 0.060795,
     "end_time": "2022-10-14T05:54:02.364277",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.303482",
     "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",
       "      <th>Title</th>\n",
       "      <th>Ticket_2letter</th>\n",
       "      <th>Ticket_len</th>\n",
       "      <th>Cabin_num</th>\n",
       "      <th>Cabin_1letter</th>\n",
       "      <th>Fam_size</th>\n",
       "      <th>Fam_type</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",
       "      <td>Mr</td>\n",
       "      <td>A/</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>Small</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",
       "      <td>Mrs</td>\n",
       "      <td>PC</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>P</td>\n",
       "      <td>2</td>\n",
       "      <td>Small</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",
       "      <td>Miss</td>\n",
       "      <td>ST</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Solo</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",
       "      <td>Mrs</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Small</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",
       "      <td>Mr</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Solo</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",
       "      <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>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Rev</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Solo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Solo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>W.</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>Mr</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Solo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>Mr</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Solo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 19 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked Title Ticket_2letter  \\\n",
       "0        0         A/5 21171   7.2500   NaN        S    Mr             A/   \n",
       "1        0          PC 17599  71.2833   C85        C   Mrs             PC   \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  Miss             ST   \n",
       "3        0            113803  53.1000  C123        S   Mrs             11   \n",
       "4        0            373450   8.0500   NaN        S    Mr             37   \n",
       "..     ...               ...      ...   ...      ...   ...            ...   \n",
       "886      0            211536  13.0000   NaN        S   Rev             21   \n",
       "887      0            112053  30.0000   B42        S  Miss             11   \n",
       "888      2        W./C. 6607  23.4500   NaN        S  Miss             W.   \n",
       "889      0            111369  30.0000  C148        C    Mr             11   \n",
       "890      0            370376   7.7500   NaN        Q    Mr             37   \n",
       "\n",
       "     Ticket_len  Cabin_num Cabin_1letter  Fam_size Fam_type  \n",
       "0             9          2             A         2    Small  \n",
       "1             8          2             P         2    Small  \n",
       "2            16          2             S         1     Solo  \n",
       "3             6          1             1         2    Small  \n",
       "4             6          1             3         1     Solo  \n",
       "..          ...        ...           ...       ...      ...  \n",
       "886           6          1             2         1     Solo  \n",
       "887           6          1             1         1     Solo  \n",
       "888          10          2             W         4    Small  \n",
       "889           6          1             1         1     Solo  \n",
       "890           6          1             3         1     Solo  \n",
       "\n",
       "[891 rows x 19 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f33360",
   "metadata": {
    "papermill": {
     "duration": 0.031205,
     "end_time": "2022-10-14T05:54:02.428321",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.397116",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Split Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "15dfa6d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:02.492376Z",
     "iopub.status.busy": "2022-10-14T05:54:02.491809Z",
     "iopub.status.idle": "2022-10-14T05:54:02.499466Z",
     "shell.execute_reply": "2022-10-14T05:54:02.498392Z"
    },
    "papermill": {
     "duration": 0.042145,
     "end_time": "2022-10-14T05:54:02.501468",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.459323",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_ds = load_data.sample(frac=0.70, random_state=1)\n",
    "\n",
    "val_ds = load_data.drop(train_ds.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7be6af03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:02.565272Z",
     "iopub.status.busy": "2022-10-14T05:54:02.564940Z",
     "iopub.status.idle": "2022-10-14T05:54:02.582348Z",
     "shell.execute_reply": "2022-10-14T05:54:02.581510Z"
    },
    "papermill": {
     "duration": 0.051484,
     "end_time": "2022-10-14T05:54:02.584203",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.532719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <td>863</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Nenkoff, Mr. Christo</td>\n",
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       "      <td>0</td>\n",
       "      <td>349234</td>\n",
       "      <td>7.8958</td>\n",
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       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
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       "      <td>Ilett, Miss. Bertha</td>\n",
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       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>681</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peters, Miss. Katie</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330935</td>\n",
       "      <td>8.1375</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Miss</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>536</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hart, Miss. Eva Miriam</td>\n",
       "      <td>female</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>F.C.C. 13529</td>\n",
       "      <td>26.2500</td>\n",
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       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>F.</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "862          863         1       1   \n",
       "223          224         0       3   \n",
       "84            85         1       2   \n",
       "680          681         0       3   \n",
       "535          536         1       2   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.0      0   \n",
       "223                               Nenkoff, Mr. Christo    male   NaN      0   \n",
       "84                                 Ilett, Miss. Bertha  female  17.0      0   \n",
       "680                                Peters, Miss. Katie  female   NaN      0   \n",
       "535                             Hart, Miss. Eva Miriam  female   7.0      0   \n",
       "\n",
       "     Parch        Ticket     Fare Cabin Embarked Title Ticket_2letter  \\\n",
       "862      0         17466  25.9292   D17        S   Mrs             17   \n",
       "223      0        349234   7.8958   NaN        S    Mr             34   \n",
       "84       0    SO/C 14885  10.5000   NaN        S  Miss             SO   \n",
       "680      0        330935   8.1375   NaN        Q  Miss             33   \n",
       "535      2  F.C.C. 13529  26.2500   NaN        S  Miss             F.   \n",
       "\n",
       "     Ticket_len  Cabin_num Cabin_1letter  Fam_size Fam_type  \n",
       "862           5          1             1         1     Solo  \n",
       "223           6          1             3         1     Solo  \n",
       "84           10          2             S         1     Solo  \n",
       "680           6          1             3         1     Solo  \n",
       "535          12          2             F         3    Small  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a22cfbd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:02.649373Z",
     "iopub.status.busy": "2022-10-14T05:54:02.648135Z",
     "iopub.status.idle": "2022-10-14T05:54:02.742092Z",
     "shell.execute_reply": "2022-10-14T05:54:02.741092Z"
    },
    "papermill": {
     "duration": 0.128496,
     "end_time": "2022-10-14T05:54:02.744052",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.615556",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/tensorflow_decision_forests/keras/core.py:1612: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  features_dataframe = dataframe.drop(label, 1)\n",
      "2022-10-14 05:54:02.677592: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds, label=label)\n",
    "val_ds = tfdf.keras.pd_dataframe_to_tf_dataset(val_ds, label=label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f537b898",
   "metadata": {
    "papermill": {
     "duration": 0.031846,
     "end_time": "2022-10-14T05:54:02.807955",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.776109",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Specify features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fd18f781",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:02.873050Z",
     "iopub.status.busy": "2022-10-14T05:54:02.872418Z",
     "iopub.status.idle": "2022-10-14T05:54:02.878758Z",
     "shell.execute_reply": "2022-10-14T05:54:02.878094Z"
    },
    "papermill": {
     "duration": 0.041185,
     "end_time": "2022-10-14T05:54:02.880617",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.839432",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pclass = tfdf.keras.FeatureUsage(name=\"Pclass\")\n",
    "fare = tfdf.keras.FeatureUsage(name=\"Fare\")\n",
    "title = tfdf.keras.FeatureUsage(name=\"Title\")\n",
    "embarked = tfdf.keras.FeatureUsage(name=\"Embarked\")\n",
    "famtype = tfdf.keras.FeatureUsage(name=\"Fam_type\")\n",
    "ticketlen = tfdf.keras.FeatureUsage(name=\"Ticket_len\")\n",
    "ticket2letter = tfdf.keras.FeatureUsage(name=\"Ticket_2letter\")\n",
    "\n",
    "all_features = [pclass, fare, title, embarked, famtype, ticketlen, ticket2letter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ac4e5e9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:02.947246Z",
     "iopub.status.busy": "2022-10-14T05:54:02.946596Z",
     "iopub.status.idle": "2022-10-14T05:54:02.979966Z",
     "shell.execute_reply": "2022-10-14T05:54:02.978136Z"
    },
    "papermill": {
     "duration": 0.069859,
     "end_time": "2022-10-14T05:54:02.982362",
     "exception": false,
     "start_time": "2022-10-14T05:54:02.912503",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Specify the model with features\n",
    "model_2 = tfdf.keras.RandomForestModel(verbose=2, features=all_features, num_trees=500, max_depth=5, exclude_non_specified_features=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9feff3d9",
   "metadata": {
    "papermill": {
     "duration": 0.031017,
     "end_time": "2022-10-14T05:54:03.045016",
     "exception": false,
     "start_time": "2022-10-14T05:54:03.013999",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "49a0bf00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:03.110150Z",
     "iopub.status.busy": "2022-10-14T05:54:03.109773Z",
     "iopub.status.idle": "2022-10-14T05:54:13.251059Z",
     "shell.execute_reply": "2022-10-14T05:54:13.249445Z"
    },
    "papermill": {
     "duration": 10.17686,
     "end_time": "2022-10-14T05:54:13.253569",
     "exception": false,
     "start_time": "2022-10-14T05:54:03.076709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1/10 [==>...........................] - ETA: 1:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-14 05:54:09.867463: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "[INFO kernel.cc:736] Start Yggdrasil model training\n",
      "[INFO kernel.cc:737] Collect training examples\n",
      "[INFO kernel.cc:392] Number of batches: 10\n",
      "[INFO kernel.cc:393] Number of examples: 624\n",
      "[INFO data_spec_inference.cc:290] 24 item(s) have been pruned (i.e. they are considered out of dictionary) for the column Ticket_2letter (29 item(s) left) because min_value_count=5 and max_number_of_unique_values=2000\n",
      "[INFO data_spec_inference.cc:290] 12 item(s) have been pruned (i.e. they are considered out of dictionary) for the column Title (4 item(s) left) because min_value_count=5 and max_number_of_unique_values=2000\n",
      "[INFO kernel.cc:759] Dataset:\n",
      "Number of records: 624\n",
      "Number of columns: 8\n",
      "\n",
      "Number of columns by type:\n",
      "\tCATEGORICAL: 5 (62.5%)\n",
      "\tNUMERICAL: 3 (37.5%)\n",
      "\n",
      "Columns:\n",
      "\n",
      "CATEGORICAL: 5 (62.5%)\n",
      "\t0: \"Embarked\" CATEGORICAL num-nas:1 (0.160256%) has-dict vocab-size:4 zero-ood-items most-frequent:\"S\" 439 (70.4655%)\n",
      "\t1: \"Fam_type\" CATEGORICAL has-dict vocab-size:5 zero-ood-items most-frequent:\"Solo\" 367 (58.8141%)\n",
      "\t4: \"Ticket_2letter\" CATEGORICAL has-dict vocab-size:30 num-oods:24 (3.84615%) most-frequent:\"34\" 86 (13.7821%)\n",
      "\t6: \"Title\" CATEGORICAL has-dict vocab-size:5 num-oods:12 (1.92308%) most-frequent:\"Mr\" 357 (57.2115%)\n",
      "\t7: \"__LABEL\" CATEGORICAL integerized vocab-size:3 no-ood-item\n",
      "\n",
      "NUMERICAL: 3 (37.5%)\n",
      "\t2: \"Fare\" NUMERICAL mean:34.0257 min:0 max:512.329 sd:54.8729\n",
      "\t3: \"Pclass\" NUMERICAL mean:2.29327 min:1 max:3 sd:0.842892\n",
      "\t5: \"Ticket_len\" NUMERICAL mean:6.84295 min:4 max:18 sd:2.8286\n",
      "\n",
      "Terminology:\n",
      "\tnas: Number of non-available (i.e. missing) values.\n",
      "\tood: Out of dictionary.\n",
      "\tmanually-defined: Attribute which type is manually defined by the user i.e. the type was not automatically inferred.\n",
      "\ttokenized: The attribute value is obtained through tokenization.\n",
      "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n",
      "\tvocab-size: Number of unique values.\n",
      "\n",
      "[INFO kernel.cc:762] Configure learner\n",
      "[INFO kernel.cc:787] Training config:\n",
      "learner: \"RANDOM_FOREST\"\n",
      "features: \"Embarked\"\n",
      "features: \"Fam_type\"\n",
      "features: \"Fare\"\n",
      "features: \"Pclass\"\n",
      "features: \"Ticket_2letter\"\n",
      "features: \"Ticket_len\"\n",
      "features: \"Title\"\n",
      "label: \"__LABEL\"\n",
      "task: CLASSIFICATION\n",
      "[yggdrasil_decision_forests.model.random_forest.proto.random_forest_config] {\n",
      "  num_trees: 500\n",
      "  decision_tree {\n",
      "    max_depth: 5\n",
      "    min_examples: 5\n",
      "    in_split_min_examples_check: true\n",
      "    missing_value_policy: GLOBAL_IMPUTATION\n",
      "    allow_na_conditions: false\n",
      "    categorical_set_greedy_forward {\n",
      "      sampling: 0.1\n",
      "      max_num_items: -1\n",
      "      min_item_frequency: 1\n",
      "    }\n",
      "    growing_strategy_local {\n",
      "    }\n",
      "    categorical {\n",
      "      cart {\n",
      "      }\n",
      "    }\n",
      "    num_candidate_attributes_ratio: -1\n",
      "    axis_aligned_split {\n",
      "    }\n",
      "    internal {\n",
      "      sorting_strategy: PRESORTED\n",
      "    }\n",
      "  }\n",
      "  winner_take_all_inference: true\n",
      "  compute_oob_performances: true\n",
      "  compute_oob_variable_importances: false\n",
      "  adapt_bootstrap_size_ratio_for_maximum_training_duration: false\n",
      "}\n",
      "\n",
      "[INFO kernel.cc:790] Deployment config:\n",
      "num_threads: 6\n",
      "\n",
      "[INFO kernel.cc:817] Train model\n",
      "[INFO random_forest.cc:315] Training random forest on 624 example(s) and 7 feature(s).\n",
      "[INFO random_forest.cc:628] Training of tree  1/500 (tree index:2) done accuracy:0.814978 logloss:6.66887\n",
      "[INFO random_forest.cc:628] Training of tree  11/500 (tree index:10) done accuracy:0.805873 logloss:4.46535\n",
      "[INFO random_forest.cc:628] Training of tree  21/500 (tree index:20) done accuracy:0.81891 logloss:2.87896\n",
      "[INFO random_forest.cc:628] Training of tree  31/500 (tree index:30) done accuracy:0.820513 logloss:2.38648\n",
      "[INFO random_forest.cc:628] Training of tree  41/500 (tree index:34) done accuracy:0.823718 logloss:2.06002\n",
      "[INFO random_forest.cc:628] Training of tree  51/500 (tree index:52) done accuracy:0.826923 logloss:1.96042\n",
      "[INFO random_forest.cc:628] Training of tree  61/500 (tree index:60) done accuracy:0.830128 logloss:1.85238\n",
      "[INFO random_forest.cc:628] Training of tree  72/500 (tree index:71) done accuracy:0.831731 logloss:1.85405\n",
      "[INFO random_forest.cc:628] Training of tree  83/500 (tree index:82) done accuracy:0.826923 logloss:1.85506\n",
      "[INFO random_forest.cc:628] Training of tree  93/500 (tree index:93) done accuracy:0.838141 logloss:1.69684\n",
      "[INFO random_forest.cc:628] Training of tree  103/500 (tree index:102) done accuracy:0.831731 logloss:1.64584\n",
      "[INFO random_forest.cc:628] Training of tree  113/500 (tree index:112) done accuracy:0.834936 logloss:1.64269\n",
      "[INFO random_forest.cc:628] Training of tree  124/500 (tree index:123) done accuracy:0.830128 logloss:1.64559\n",
      "[INFO random_forest.cc:628] Training of tree  134/500 (tree index:133) done accuracy:0.826923 logloss:1.64684\n",
      "[INFO random_forest.cc:628] Training of tree  144/500 (tree index:143) done accuracy:0.828526 logloss:1.59698\n",
      "[INFO random_forest.cc:628] Training of tree  154/500 (tree index:153) done accuracy:0.830128 logloss:1.49273\n",
      "[INFO random_forest.cc:628] Training of tree  164/500 (tree index:163) done accuracy:0.830128 logloss:1.44243\n",
      "[INFO random_forest.cc:628] Training of tree  174/500 (tree index:174) done accuracy:0.828526 logloss:1.44479\n",
      "[INFO random_forest.cc:628] Training of tree  184/500 (tree index:183) done accuracy:0.831731 logloss:1.44463\n",
      "[INFO random_forest.cc:628] Training of tree  194/500 (tree index:193) done accuracy:0.825321 logloss:1.44318\n",
      "[INFO random_forest.cc:628] Training of tree  204/500 (tree index:205) done accuracy:0.830128 logloss:1.44387\n",
      "[INFO random_forest.cc:628] Training of tree  215/500 (tree index:214) done accuracy:0.830128 logloss:1.39238\n",
      "[INFO random_forest.cc:628] Training of tree  225/500 (tree index:225) done accuracy:0.826923 logloss:1.39344\n",
      "[INFO random_forest.cc:628] Training of tree  235/500 (tree index:234) done accuracy:0.828526 logloss:1.39336\n",
      "[INFO random_forest.cc:628] Training of tree  245/500 (tree index:240) done accuracy:0.826923 logloss:1.39287\n",
      "[INFO random_forest.cc:628] Training of tree  255/500 (tree index:256) done accuracy:0.825321 logloss:1.39373\n",
      "[INFO random_forest.cc:628] Training of tree  265/500 (tree index:264) done accuracy:0.826923 logloss:1.39278\n",
      "[INFO random_forest.cc:628] Training of tree  275/500 (tree index:277) done accuracy:0.826923 logloss:1.39313\n",
      "[INFO random_forest.cc:628] Training of tree  285/500 (tree index:285) done accuracy:0.825321 logloss:1.39256\n",
      "[INFO random_forest.cc:628] Training of tree  295/500 (tree index:295) done accuracy:0.826923 logloss:1.3917\n",
      "[INFO random_forest.cc:628] Training of tree  305/500 (tree index:305) done accuracy:0.825321 logloss:1.39061\n",
      "[INFO random_forest.cc:628] Training of tree  315/500 (tree index:315) done accuracy:0.826923 logloss:1.39097\n",
      "[INFO random_forest.cc:628] Training of tree  325/500 (tree index:324) done accuracy:0.826923 logloss:1.39053\n",
      "[INFO random_forest.cc:628] Training of tree  335/500 (tree index:334) done accuracy:0.823718 logloss:1.34107\n",
      "[INFO random_forest.cc:628] Training of tree  345/500 (tree index:347) done accuracy:0.825321 logloss:1.34015\n",
      "[INFO random_forest.cc:628] Training of tree  355/500 (tree index:355) done accuracy:0.823718 logloss:1.33955\n",
      "[INFO random_forest.cc:628] Training of tree  365/500 (tree index:365) done accuracy:0.825321 logloss:1.34002\n",
      "[INFO random_forest.cc:628] Training of tree  377/500 (tree index:345) done accuracy:0.825321 logloss:1.2896\n",
      "[INFO random_forest.cc:628] Training of tree  387/500 (tree index:386) done accuracy:0.823718 logloss:1.28971\n",
      "[INFO random_forest.cc:628] Training of tree  397/500 (tree index:398) done accuracy:0.825321 logloss:1.23917\n",
      "[INFO random_forest.cc:628] Training of tree  407/500 (tree index:406) done accuracy:0.825321 logloss:1.23897\n",
      "[INFO random_forest.cc:628] Training of tree  417/500 (tree index:416) done accuracy:0.825321 logloss:1.2391\n",
      "[INFO random_forest.cc:628] Training of tree  428/500 (tree index:427) done accuracy:0.826923 logloss:1.23987\n",
      "[INFO random_forest.cc:628] Training of tree  438/500 (tree index:438) done accuracy:0.825321 logloss:1.2402\n",
      "[INFO random_forest.cc:628] Training of tree  448/500 (tree index:448) done accuracy:0.828526 logloss:1.2405\n",
      "[INFO random_forest.cc:628] Training of tree  458/500 (tree index:457) done accuracy:0.826923 logloss:1.24062\n",
      "[INFO random_forest.cc:628] Training of tree  468/500 (tree index:467) done accuracy:0.826923 logloss:1.24104\n",
      "[INFO random_forest.cc:628] Training of tree  478/500 (tree index:477) done accuracy:0.826923 logloss:1.24178\n",
      "[INFO random_forest.cc:628] Training of tree  488/500 (tree index:489) done accuracy:0.828526 logloss:1.24197\n",
      "[INFO random_forest.cc:628] Training of tree  498/500 (tree index:497) done accuracy:0.826923 logloss:1.24206\n",
      "[INFO random_forest.cc:628] Training of tree  500/500 (tree index:499) done accuracy:0.826923 logloss:1.24193\n",
      "[INFO random_forest.cc:696] Final OOB metrics: accuracy:0.826923 logloss:1.24193\n",
      "[INFO kernel.cc:828] Export model in log directory: /tmp/tmpbp5te9pc\n",
      "[INFO kernel.cc:836] Save model in resources\n",
      "[INFO kernel.cc:988] Loading model from path\n",
      "[INFO decision_forest.cc:590] Model loaded with 500 root(s), 13530 node(s), and 7 input feature(s).\n",
      "[INFO abstract_model.cc:993] Engine \"RandomForestOptPred\" built\n",
      "[INFO kernel.cc:848] Use fast generic engine\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 10s 333ms/step - val_loss: 0.0000e+00 - val_accuracy: 0.8240\n",
      "5/5 [==============================] - 0s 3ms/step - loss: 0.0000e+00 - accuracy: 0.8240\n",
      "{'loss': 0.0, 'accuracy': 0.8239700198173523}\n"
     ]
    }
   ],
   "source": [
    "model_2.compile(metrics=[\"accuracy\"])\n",
    "model_2.fit(x=train_ds, validation_data=val_ds)\n",
    "\n",
    "print(model_2.evaluate(val_ds, return_dict=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6a52e8",
   "metadata": {
    "papermill": {
     "duration": 0.032099,
     "end_time": "2022-10-14T05:54:13.318070",
     "exception": false,
     "start_time": "2022-10-14T05:54:13.285971",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "92d268be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:13.384214Z",
     "iopub.status.busy": "2022-10-14T05:54:13.383838Z",
     "iopub.status.idle": "2022-10-14T05:54:14.804408Z",
     "shell.execute_reply": "2022-10-14T05:54:14.803217Z"
    },
    "papermill": {
     "duration": 1.456825,
     "end_time": "2022-10-14T05:54:14.806939",
     "exception": false,
     "start_time": "2022-10-14T05:54:13.350114",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-14 05:54:13.964139: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    }
   ],
   "source": [
    "model_2.save(\"titanic_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc95658",
   "metadata": {
    "papermill": {
     "duration": 0.03244,
     "end_time": "2022-10-14T05:54:14.872510",
     "exception": false,
     "start_time": "2022-10-14T05:54:14.840070",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Plot the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5c6fb56e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:14.940773Z",
     "iopub.status.busy": "2022-10-14T05:54:14.940343Z",
     "iopub.status.idle": "2022-10-14T05:54:14.963571Z",
     "shell.execute_reply": "2022-10-14T05:54:14.962652Z"
    },
    "papermill": {
     "duration": 0.059905,
     "end_time": "2022-10-14T05:54:14.965433",
     "exception": false,
     "start_time": "2022-10-14T05:54:14.905528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script src=\"https://d3js.org/d3.v6.min.js\"></script>\n",
       "<div id=\"tree_plot_0602c51971854fffae5aa99b39aebf25\"></div>\n",
       "<script>\n",
       "/*\n",
       " * Copyright 2021 Google LLC.\n",
       " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       " * you may not use this file except in compliance with the License.\n",
       " * You may obtain a copy of the License at\n",
       " *\n",
       " *     https://www.apache.org/licenses/LICENSE-2.0\n",
       " *\n",
       " * Unless required by applicable law or agreed to in writing, software\n",
       " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       " * See the License for the specific language governing permissions and\n",
       " * limitations under the License.\n",
       " */\n",
       "\n",
       "/**\n",
       " *  Plotting of decision trees generated by TF-DF.\n",
       " *\n",
       " *  A tree is a recursive structure of node objects.\n",
       " *  A node contains one or more of the following components:\n",
       " *\n",
       " *    - A value: Representing the output of the node. If the node is not a leaf,\n",
       " *      the value is only present for analysis i.e. it is not used for\n",
       " *      predictions.\n",
       " *\n",
       " *    - A condition : For non-leaf nodes, the condition (also known as split)\n",
       " *      defines a binary test to branch to the positive or negative child.\n",
       " *\n",
       " *    - An explanation: Generally a plot showing the relation between the label\n",
       " *      and the condition to give insights about the effect of the condition.\n",
       " *\n",
       " *    - Two children : For non-leaf nodes, the children nodes. The first\n",
       " *      children (i.e. \"node.children[0]\") is the negative children (drawn in\n",
       " *      red). The second children is the positive one (drawn in green).\n",
       " *\n",
       " */\n",
       "\n",
       "/**\n",
       " * Plots a single decision tree into a DOM element.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!tree} raw_tree Recursive tree structure.\n",
       " * @param {string} canvas_id Id of the output dom element.\n",
       " */\n",
       "function display_tree(options, raw_tree, canvas_id) {\n",
       "  console.log(options);\n",
       "\n",
       "  // Determine the node placement.\n",
       "  const tree_struct = d3.tree().nodeSize(\n",
       "      [options.node_y_offset, options.node_x_offset])(d3.hierarchy(raw_tree));\n",
       "\n",
       "  // Boundaries of the node placement.\n",
       "  let x_min = Infinity;\n",
       "  let x_max = -x_min;\n",
       "  let y_min = Infinity;\n",
       "  let y_max = -x_min;\n",
       "\n",
       "  tree_struct.each(d => {\n",
       "    if (d.x > x_max) x_max = d.x;\n",
       "    if (d.x < x_min) x_min = d.x;\n",
       "    if (d.y > y_max) y_max = d.y;\n",
       "    if (d.y < y_min) y_min = d.y;\n",
       "  });\n",
       "\n",
       "  // Size of the plot.\n",
       "  const width = y_max - y_min + options.node_x_size + options.margin * 2;\n",
       "  const height = x_max - x_min + options.node_y_size + options.margin * 2 +\n",
       "      options.node_y_offset - options.node_y_size;\n",
       "\n",
       "  const plot = d3.select(canvas_id);\n",
       "\n",
       "  // Tool tip\n",
       "  options.tooltip = plot.append('div')\n",
       "                        .attr('width', 100)\n",
       "                        .attr('height', 100)\n",
       "                        .style('padding', '4px')\n",
       "                        .style('background', '#fff')\n",
       "                        .style('box-shadow', '4px 4px 0px rgba(0,0,0,0.1)')\n",
       "                        .style('border', '1px solid black')\n",
       "                        .style('font-family', 'sans-serif')\n",
       "                        .style('font-size', options.font_size)\n",
       "                        .style('position', 'absolute')\n",
       "                        .style('z-index', '10')\n",
       "                        .attr('pointer-events', 'none')\n",
       "                        .style('display', 'none');\n",
       "\n",
       "  // Create canvas\n",
       "  const svg = plot.append('svg').attr('width', width).attr('height', height);\n",
       "  const graph =\n",
       "      svg.style('overflow', 'visible')\n",
       "          .append('g')\n",
       "          .attr('font-family', 'sans-serif')\n",
       "          .attr('font-size', options.font_size)\n",
       "          .attr(\n",
       "              'transform',\n",
       "              () => `translate(${options.margin},${\n",
       "                  - x_min + options.node_y_offset / 2 + options.margin})`);\n",
       "\n",
       "  // Plot bounding box.\n",
       "  if (options.show_plot_bounding_box) {\n",
       "    svg.append('rect')\n",
       "        .attr('width', width)\n",
       "        .attr('height', height)\n",
       "        .attr('fill', 'none')\n",
       "        .attr('stroke-width', 1.0)\n",
       "        .attr('stroke', 'black');\n",
       "  }\n",
       "\n",
       "  // Draw the edges.\n",
       "  display_edges(options, graph, tree_struct);\n",
       "\n",
       "  // Draw the nodes.\n",
       "  display_nodes(options, graph, tree_struct);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Draw the nodes of the tree.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!graph} graph D3 search handle containing the graph.\n",
       " * @param {!tree_struct} tree_struct Structure of the tree (node placement,\n",
       " *     data, etc.).\n",
       " */\n",
       "function display_nodes(options, graph, tree_struct) {\n",
       "  const nodes = graph.append('g')\n",
       "                    .selectAll('g')\n",
       "                    .data(tree_struct.descendants())\n",
       "                    .join('g')\n",
       "                    .attr('transform', d => `translate(${d.y},${d.x})`);\n",
       "\n",
       "  nodes.append('rect')\n",
       "      .attr('x', 0.5)\n",
       "      .attr('y', 0.5)\n",
       "      .attr('width', options.node_x_size)\n",
       "      .attr('height', options.node_y_size)\n",
       "      .attr('stroke', 'lightgrey')\n",
       "      .attr('stroke-width', 1)\n",
       "      .attr('fill', 'white')\n",
       "      .attr('y', -options.node_y_size / 2);\n",
       "\n",
       "  // Brackets on the right of condition nodes without children.\n",
       "  non_leaf_node_without_children =\n",
       "      nodes.filter(node => node.data.condition != null && node.children == null)\n",
       "          .append('g')\n",
       "          .attr('transform', `translate(${options.node_x_size},0)`);\n",
       "\n",
       "  non_leaf_node_without_children.append('path')\n",
       "      .attr('d', 'M0,0 C 10,0 0,10 10,10')\n",
       "      .attr('fill', 'none')\n",
       "      .attr('stroke-width', 1.0)\n",
       "      .attr('stroke', '#F00');\n",
       "\n",
       "  non_leaf_node_without_children.append('path')\n",
       "      .attr('d', 'M0,0 C 10,0 0,-10 10,-10')\n",
       "      .attr('fill', 'none')\n",
       "      .attr('stroke-width', 1.0)\n",
       "      .attr('stroke', '#0F0');\n",
       "\n",
       "  const node_content = nodes.append('g').attr(\n",
       "      'transform',\n",
       "      `translate(0,${options.node_padding - options.node_y_size / 2})`);\n",
       "\n",
       "  node_content.append(node => create_node_element(options, node));\n",
       "}\n",
       "\n",
       "/**\n",
       " * Creates the D3 content for a single node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!node} node Node to draw.\n",
       " * @return {!d3} D3 content.\n",
       " */\n",
       "function create_node_element(options, node) {\n",
       "  // Output accumulator.\n",
       "  let output = {\n",
       "    // Content to draw.\n",
       "    content: d3.create('svg:g'),\n",
       "    // Vertical offset to the next element to draw.\n",
       "    vertical_offset: 0\n",
       "  };\n",
       "\n",
       "  // Conditions.\n",
       "  if (node.data.condition != null) {\n",
       "    display_condition(options, node.data.condition, output);\n",
       "  }\n",
       "\n",
       "  // Values.\n",
       "  if (node.data.value != null) {\n",
       "    display_value(options, node.data.value, output);\n",
       "  }\n",
       "\n",
       "  // Explanations.\n",
       "  if (node.data.explanation != null) {\n",
       "    display_explanation(options, node.data.explanation, output);\n",
       "  }\n",
       "\n",
       "  return output.content.node();\n",
       "}\n",
       "\n",
       "\n",
       "/**\n",
       " * Adds a single line of text inside of a node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {string} text Text to display.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_node_text(options, text, output) {\n",
       "  output.content.append('text')\n",
       "      .attr('x', options.node_padding)\n",
       "      .attr('y', output.vertical_offset)\n",
       "      .attr('alignment-baseline', 'hanging')\n",
       "      .text(text);\n",
       "  output.vertical_offset += 10;\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds a single line of text inside of a node with a tooltip.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {string} text Text to display.\n",
       " * @param {string} tooltip Text in the Tooltip.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_node_text_with_tooltip(options, text, tooltip, output) {\n",
       "  const item = output.content.append('text')\n",
       "                   .attr('x', options.node_padding)\n",
       "                   .attr('alignment-baseline', 'hanging')\n",
       "                   .text(text);\n",
       "\n",
       "  add_tooltip(options, item, () => tooltip);\n",
       "  output.vertical_offset += 10;\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds a tooltip to a dom element.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!dom} target Dom element to equip with a tooltip.\n",
       " * @param {!func} get_content Generates the html content of the tooltip.\n",
       " */\n",
       "function add_tooltip(options, target, get_content) {\n",
       "  function show(d) {\n",
       "    options.tooltip.style('display', 'block');\n",
       "    options.tooltip.html(get_content());\n",
       "  }\n",
       "\n",
       "  function hide(d) {\n",
       "    options.tooltip.style('display', 'none');\n",
       "  }\n",
       "\n",
       "  function move(d) {\n",
       "    options.tooltip.style('display', 'block');\n",
       "    options.tooltip.style('left', (d.pageX + 5) + 'px');\n",
       "    options.tooltip.style('top', d.pageY + 'px');\n",
       "  }\n",
       "\n",
       "  target.on('mouseover', show);\n",
       "  target.on('mouseout', hide);\n",
       "  target.on('mousemove', move);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds a condition inside of a node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!condition} condition Condition to display.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_condition(options, condition, output) {\n",
       "  threshold_format = d3.format('r');\n",
       "\n",
       "  if (condition.type === 'IS_MISSING') {\n",
       "    display_node_text(options, `${condition.attribute} is missing`, output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'IS_TRUE') {\n",
       "    display_node_text(options, `${condition.attribute} is true`, output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'NUMERICAL_IS_HIGHER_THAN') {\n",
       "    format = d3.format('r');\n",
       "    display_node_text(\n",
       "        options,\n",
       "        `${condition.attribute} >= ${threshold_format(condition.threshold)}`,\n",
       "        output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'CATEGORICAL_IS_IN') {\n",
       "    display_node_text_with_tooltip(\n",
       "        options, `${condition.attribute} in [...]`,\n",
       "        `${condition.attribute} in [${condition.mask}]`, output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'CATEGORICAL_SET_CONTAINS') {\n",
       "    display_node_text_with_tooltip(\n",
       "        options, `${condition.attribute} intersect [...]`,\n",
       "        `${condition.attribute} intersect [${condition.mask}]`, output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (condition.type === 'NUMERICAL_SPARSE_OBLIQUE') {\n",
       "    display_node_text_with_tooltip(\n",
       "        options, `Sparse oblique split...`,\n",
       "        `[${condition.attributes}]*[${condition.weights}]>=${\n",
       "            threshold_format(condition.threshold)}`,\n",
       "        output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  display_node_text(\n",
       "      options, `Non supported condition ${condition.type}`, output);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds a value inside of a node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!value} value Value to display.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_value(options, value, output) {\n",
       "  if (value.type === 'PROBABILITY') {\n",
       "    const left_margin = 0;\n",
       "    const right_margin = 50;\n",
       "    const plot_width = options.node_x_size - options.node_padding * 2 -\n",
       "        left_margin - right_margin;\n",
       "\n",
       "    let cusum = Array.from(d3.cumsum(value.distribution));\n",
       "    cusum.unshift(0);\n",
       "    const distribution_plot = output.content.append('g').attr(\n",
       "        'transform', `translate(0,${output.vertical_offset + 0.5})`);\n",
       "\n",
       "    distribution_plot.selectAll('rect')\n",
       "        .data(value.distribution)\n",
       "        .join('rect')\n",
       "        .attr('height', 10)\n",
       "        .attr(\n",
       "            'x',\n",
       "            (d, i) =>\n",
       "                (cusum[i] * plot_width + left_margin + options.node_padding))\n",
       "        .attr('width', (d, i) => d * plot_width)\n",
       "        .style('fill', (d, i) => d3.schemeSet1[i]);\n",
       "\n",
       "    const num_examples =\n",
       "        output.content.append('g')\n",
       "            .attr('transform', `translate(0,${output.vertical_offset})`)\n",
       "            .append('text')\n",
       "            .attr('x', options.node_x_size - options.node_padding)\n",
       "            .attr('alignment-baseline', 'hanging')\n",
       "            .attr('text-anchor', 'end')\n",
       "            .text(`(${value.num_examples})`);\n",
       "\n",
       "    const distribution_details = d3.create('ul');\n",
       "    distribution_details.selectAll('li')\n",
       "        .data(value.distribution)\n",
       "        .join('li')\n",
       "        .append('span')\n",
       "        .text(\n",
       "            (d, i) =>\n",
       "                'class ' + i + ': ' + d3.format('.3%')(value.distribution[i]));\n",
       "\n",
       "    add_tooltip(options, distribution_plot, () => distribution_details.html());\n",
       "    add_tooltip(options, num_examples, () => 'Number of examples');\n",
       "\n",
       "    output.vertical_offset += 10;\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  if (value.type === 'REGRESSION') {\n",
       "    display_node_text(\n",
       "        options,\n",
       "        'value: ' + d3.format('r')(value.value) + ` (` +\n",
       "            d3.format('.6')(value.num_examples) + `)`,\n",
       "        output);\n",
       "    return;\n",
       "  }\n",
       "\n",
       "  display_node_text(options, `Non supported value ${value.type}`, output);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Adds an explanation inside of a node.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!explanation} explanation Explanation to display.\n",
       " * @param {!output} output Output display accumulator.\n",
       " */\n",
       "function display_explanation(options, explanation, output) {\n",
       "  // Margin before the explanation.\n",
       "  output.vertical_offset += 10;\n",
       "\n",
       "  display_node_text(\n",
       "      options, `Non supported explanation ${explanation.type}`, output);\n",
       "}\n",
       "\n",
       "\n",
       "/**\n",
       " * Draw the edges of the tree.\n",
       " * @param {!options} options Dictionary of configurations.\n",
       " * @param {!graph} graph D3 search handle containing the graph.\n",
       " * @param {!tree_struct} tree_struct Structure of the tree (node placement,\n",
       " *     data, etc.).\n",
       " */\n",
       "function display_edges(options, graph, tree_struct) {\n",
       "  // Draw an edge between a parent and a child node with a bezier.\n",
       "  function draw_single_edge(d) {\n",
       "    return 'M' + (d.source.y + options.node_x_size) + ',' + d.source.x + ' C' +\n",
       "        (d.source.y + options.node_x_size + options.edge_rounding) + ',' +\n",
       "        d.source.x + ' ' + (d.target.y - options.edge_rounding) + ',' +\n",
       "        d.target.x + ' ' + d.target.y + ',' + d.target.x;\n",
       "  }\n",
       "\n",
       "  graph.append('g')\n",
       "      .attr('fill', 'none')\n",
       "      .attr('stroke-width', 1.2)\n",
       "      .selectAll('path')\n",
       "      .data(tree_struct.links())\n",
       "      .join('path')\n",
       "      .attr('d', draw_single_edge)\n",
       "      .attr(\n",
       "          'stroke', d => (d.target === d.source.children[0]) ? '#0F0' : '#F00');\n",
       "}\n",
       "\n",
       "display_tree({\"margin\": 10, \"node_x_size\": 160, \"node_y_size\": 28, \"node_x_offset\": 180, \"node_y_offset\": 33, \"font_size\": 10, \"edge_rounding\": 20, \"node_padding\": 2, \"show_plot_bounding_box\": false}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.592948717948718, 0.40705128205128205], \"num_examples\": 624.0}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Title\", \"mask\": [\"<OOD>\", \"Miss\", \"Mrs\", \"Master\"]}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.2730627306273063, 0.7269372693726938], \"num_examples\": 271.0}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Fam_type\", \"mask\": [\"Solo\", \"Small\"]}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.17333333333333334, 0.8266666666666667], \"num_examples\": 225.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Pclass\", \"threshold\": 2.5}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.32558139534883723, 0.6744186046511628], \"num_examples\": 86.0}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Ticket_2letter\", \"mask\": [\"<OOD>\", \"26\", \"31\", \"C.\", \"ST\", \"37\", \"35\", \"CA\", \"38\"]}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.047619047619047616, 0.9523809523809523], \"num_examples\": 42.0}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.5909090909090909, 0.4090909090909091], \"num_examples\": 44.0}}]}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.07913669064748201, 0.920863309352518], \"num_examples\": 139.0}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Title\", \"mask\": [\"Miss\", \"Mrs\", \"Master\"]}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.017241379310344827, 0.9827586206896551], \"num_examples\": 116.0}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.391304347826087, 0.6086956521739131], \"num_examples\": 23.0}}]}]}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.7608695652173914, 0.2391304347826087], \"num_examples\": 46.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Pclass\", \"threshold\": 2.5}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.8974358974358975, 0.10256410256410256], \"num_examples\": 39.0}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Ticket_2letter\", \"mask\": [\"34\", \"31\"]}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.8571428571428571, 0.14285714285714285], \"num_examples\": 28.0}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [1.0, 0.0], \"num_examples\": 11.0}}]}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.0, 1.0], \"num_examples\": 7.0}}]}]}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.8385269121813032, 0.16147308781869688], \"num_examples\": 353.0}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Ticket_2letter\", \"mask\": [\"11\", \"26\", \"PC\", \"36\", \"24\", \"ST\", \"19\", \"17\", \"29\", \"13\", \"16\"]}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.6589147286821705, 0.34108527131782945], \"num_examples\": 129.0}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Fam_type\", \"mask\": [\"Small\"]}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.5476190476190477, 0.4523809523809524], \"num_examples\": 42.0}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Ticket_2letter\", \"mask\": [\"11\", \"36\", \"19\", \"17\", \"29\", \"13\"]}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.391304347826087, 0.6086956521739131], \"num_examples\": 23.0}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.7368421052631579, 0.2631578947368421], \"num_examples\": 19.0}}]}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.7126436781609196, 0.28735632183908044], \"num_examples\": 87.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Fare\", \"threshold\": 7.175000190734863}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.6794871794871795, 0.32051282051282054], \"num_examples\": 78.0}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [1.0, 0.0], \"num_examples\": 9.0}}]}]}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.9419642857142857, 0.05803571428571429], \"num_examples\": 224.0}, \"condition\": {\"type\": \"NUMERICAL_IS_HIGHER_THAN\", \"attribute\": \"Fare\", \"threshold\": 11.75}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [1.0, 0.0], \"num_examples\": 64.0}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.91875, 0.08125], \"num_examples\": 160.0}, \"condition\": {\"type\": \"CATEGORICAL_IS_IN\", \"attribute\": \"Ticket_2letter\", \"mask\": [\"<OOD>\", \"34\", \"31\", \"SO\", \"S.\"]}, \"children\": [{\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [0.8828828828828829, 0.11711711711711711], \"num_examples\": 111.0}}, {\"value\": {\"type\": \"PROBABILITY\", \"distribution\": [1.0, 0.0], \"num_examples\": 49.0}}]}]}]}]}, \"#tree_plot_0602c51971854fffae5aa99b39aebf25\")\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfdf.model_plotter.plot_model_in_colab(model_2, tree_idx=0, max_depth=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d3261b",
   "metadata": {
    "papermill": {
     "duration": 0.033314,
     "end_time": "2022-10-14T05:54:15.032458",
     "exception": false,
     "start_time": "2022-10-14T05:54:14.999144",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "48a74268",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:15.100840Z",
     "iopub.status.busy": "2022-10-14T05:54:15.100179Z",
     "iopub.status.idle": "2022-10-14T05:54:15.589528Z",
     "shell.execute_reply": "2022-10-14T05:54:15.588769Z"
    },
    "papermill": {
     "duration": 0.526971,
     "end_time": "2022-10-14T05:54:15.592354",
     "exception": false,
     "start_time": "2022-10-14T05:54:15.065383",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"random_forest_model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "Total params: 1\n",
      "Trainable params: 0\n",
      "Non-trainable params: 1\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO kernel.cc:988] Loading model from path\n",
      "[INFO decision_forest.cc:590] Model loaded with 500 root(s), 13530 node(s), and 7 input feature(s).\n",
      "[INFO kernel.cc:848] Use fast generic engine\n"
     ]
    }
   ],
   "source": [
    "evaluator_model = tf.keras.models.load_model('titanic_model')\n",
    "\n",
    "evaluator_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecb6f65",
   "metadata": {
    "papermill": {
     "duration": 0.033073,
     "end_time": "2022-10-14T05:54:15.659557",
     "exception": false,
     "start_time": "2022-10-14T05:54:15.626484",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "85e03c73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:15.728592Z",
     "iopub.status.busy": "2022-10-14T05:54:15.727813Z",
     "iopub.status.idle": "2022-10-14T05:54:15.733020Z",
     "shell.execute_reply": "2022-10-14T05:54:15.732234Z"
    },
    "papermill": {
     "duration": 0.042253,
     "end_time": "2022-10-14T05:54:15.735168",
     "exception": false,
     "start_time": "2022-10-14T05:54:15.692915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ids = testds.pop('PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b3ec16a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:15.807287Z",
     "iopub.status.busy": "2022-10-14T05:54:15.806874Z",
     "iopub.status.idle": "2022-10-14T05:54:15.834157Z",
     "shell.execute_reply": "2022-10-14T05:54:15.832873Z"
    },
    "papermill": {
     "duration": 0.06736,
     "end_time": "2022-10-14T05:54:15.836993",
     "exception": false,
     "start_time": "2022-10-14T05:54:15.769633",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "testds = tfdf.keras.pd_dataframe_to_tf_dataset(testds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "87a12404",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:15.903508Z",
     "iopub.status.busy": "2022-10-14T05:54:15.903176Z",
     "iopub.status.idle": "2022-10-14T05:54:15.914570Z",
     "shell.execute_reply": "2022-10-14T05:54:15.913374Z"
    },
    "papermill": {
     "duration": 0.047254,
     "end_time": "2022-10-14T05:54:15.916865",
     "exception": false,
     "start_time": "2022-10-14T05:54:15.869611",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "check =pd.read_csv('/kaggle/input/titanic/gender_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a5c82b64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:15.986525Z",
     "iopub.status.busy": "2022-10-14T05:54:15.985289Z",
     "iopub.status.idle": "2022-10-14T05:54:16.182609Z",
     "shell.execute_reply": "2022-10-14T05:54:16.181957Z"
    },
    "papermill": {
     "duration": 0.234135,
     "end_time": "2022-10-14T05:54:16.184841",
     "exception": false,
     "start_time": "2022-10-14T05:54:15.950706",
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1\n",
       "5          897         0\n",
       "6          898         1\n",
       "7          899         0\n",
       "8          900         1\n",
       "9          901         0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocessing of test data, fit model\n",
    "pred =  model_2.predict(testds)\n",
    "thresh = 0.5\n",
    "pred_c = tf.cast(pred > thresh, tf.int64)\n",
    "pred_num = tf.reshape(pred_c,[len(pred_c)]).numpy()\n",
    "# Save test predictions to file\n",
    "output = pd.DataFrame({'PassengerId':ids,'Survived': pred_num.squeeze()})\n",
    "output.dtypes\n",
    "output.to_csv('submission.csv', index=False)\n",
    "output.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cfead21b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T05:54:16.253889Z",
     "iopub.status.busy": "2022-10-14T05:54:16.252430Z",
     "iopub.status.idle": "2022-10-14T05:54:16.257821Z",
     "shell.execute_reply": "2022-10-14T05:54:16.256785Z"
    },
    "papermill": {
     "duration": 0.042024,
     "end_time": "2022-10-14T05:54:16.260083",
     "exception": false,
     "start_time": "2022-10-14T05:54:16.218059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# os.remove(\"/kaggle/working/submission.csv\")"
   ]
  }
 ],
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