titanic_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic - Machine Learning from Disaster <a id='top'></a>\n",
    "Welcome! This notebook is going to build a model that predicts survival on the Titanic. The data used to build this model is collected from the [Kaggle's Titanic competition](https://www.kaggle.com/c/titanic/overview). \n",
    "\n",
    "The data is split into two groups: the training set (train.csv) and the test set (test.csv). The training set should be used to build the machine learning models. The test set should only be used for evaluating the model's performance.\n",
    "\n",
    "Data dictionary:\n",
    "\n",
    "| Variable | Definition | Key |\n",
    "|----------|------------|-----|\n",
    "| survival | Survival | 0 = No, 1 = Yes |\n",
    "| pclass | Ticket class | 1 = 1st, 2 = 2nd, 3 = 3rd |\n",
    "| sex | Sex | |\n",
    "| age | Age in years | |\n",
    "| sibsp | # of siblings / spouses aboard the Titanic | |\n",
    "| parch | # of parents / children aboard the Titanic | |\n",
    "| ticket | Ticket number | |\n",
    "| fare | Passenger fare | |\n",
    "| cabin | Cabin number | |\n",
    "| embark | Port of Embarkation | C = Cherbourg, Q = Queenstown, S = Southampton |\n",
    "\n",
    "Table of contents:\n",
    "1. [Data Exploratory](#eda)\n",
    "2. [Data Visualization](#data-visualization)\n",
    "3. [Handle Missing Data](#handle-na)\n",
    "4. [Preprocessing](#preprocessing)\n",
    "5. [Building Models](#build-models)\n",
    "6. [Load the test data](#load-test)\n",
    "7. [Prepare the test data](#prepare-test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import basic libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "\n",
    "# Preprocessing\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# Model selection\n",
    "from sklearn.model_selection import cross_validate, GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "# Import the machine learning models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"darkgrid\")\n",
    "plt.style.use(\"seaborn-darkgrid\")\n",
    "pio.renderers.default = \"notebook_connected\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploratory <a id='eda'></a>\n",
    "[Back to top](#top)\n",
    "\n",
    "Import the train.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"./data/train.csv\")\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>891.0</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>1.00</td>\n",
       "      <td>223.5000</td>\n",
       "      <td>446.0000</td>\n",
       "      <td>668.5</td>\n",
       "      <td>891.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>891.0</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>714.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count        mean         std   min       25%       50%    75%  \\\n",
       "PassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \n",
       "Survived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \n",
       "Pclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \n",
       "Age          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \n",
       "SibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \n",
       "Parch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \n",
       "Fare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n",
       "\n",
       "                  max  \n",
       "PassengerId  891.0000  \n",
       "Survived       1.0000  \n",
       "Pclass         3.0000  \n",
       "Age           80.0000  \n",
       "SibSp          8.0000  \n",
       "Parch          6.0000  \n",
       "Fare         512.3292  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization <a id='data-visualization'></a>\n",
    "[Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 5), dpi=200)\n",
    "sns.heatmap(train.corr(), annot=True, cmap='viridis')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.6.3.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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   "source": [
    "fig = px.histogram(\n",
    "    data_frame=train, x=\"Survived\", title=\"How Many Died, How Many Survived?\"\n",
    ")\n",
    "fig.update_layout(xaxis={\"type\": \"category\"})\n",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age distribution of male and female passengers aboard the Titanic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "fig = px.histogram(\n",
    "    data_frame=train,\n",
    "    x=\"Age\",\n",
    "    color=\"Sex\",\n",
    "    barmode=\"group\",\n",
    "    marginal=\"box\",\n",
    "    title=\"Age Distribution of Male and Female Passengers aboard the Titanic\",\n",
    ")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of people aboard the Titanic were in their 20s and 30s.\n",
    "\n",
    "Age distribution of passengers who did not survive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "fig = px.histogram(\n",
    "    data_frame=train[train[\"Survived\"] == 0],\n",
    "    x=\"Age\",\n",
    "    color=\"Sex\",\n",
    "    barmode=\"group\",\n",
    "    marginal=\"box\",\n",
    "    title=\"Age distribution of Passengers whom did not survive the Disaster\",\n",
    ")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passengers who died were mostly in their late 10s and early 40s, and were mainly male passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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   ],
   "source": [
    "fig = px.histogram(data_frame=train[train['Survived'] == 0], x='Sex', title='Number of Deceased Passengers')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like a lot of male passengers died with the ship, nearly 6 times greater than that of female passengers.\n",
    "\n",
    "Let's look at survival status for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "fig = px.box(\n",
    "    data_frame=train,\n",
    "    x=\"Pclass\",\n",
    "    y=\"Age\",\n",
    "    color=\"Survived\",\n",
    "    title=\"Age distribution per Class split by Survival status\",\n",
    ")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there is a same pattern for all three classes that older people did not survive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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   "source": [
    "fig = px.histogram(\n",
    "    data_frame=train,\n",
    "    x=\"Pclass\",\n",
    "    color=\"Survived\",\n",
    "    barmode=\"group\",\n",
    "    title=\"Number of Passengers per Class Split by Survival status\",\n",
    ")\n",
    "fig.show()\n"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the third class accounted for more deaths than the other two classes.\n",
    "\n",
    "Now I want to gain insight on the ports of embarkation. Which port has the most passengers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(\n",
    "    data_frame=train,\n",
    "    x=\"Embarked\",\n",
    "    color=\"Survived\",\n",
    "    title=\"Number of Passengers at each Port Split by Survival status\",\n",
    ")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of passengers boarded the Titanic at Southampton port.\n",
    "\n",
    "Finally, probably not relevant, but I want to check if `SibSp`, `Parch` and `Fare` affect `Survived` or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp      -0.035322\n",
       "Parch       0.081629\n",
       "Fare        0.257307\n",
       "Survived    1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unrelevant_features = train[[\"SibSp\", \"Parch\", \"Fare\", \"Survived\"]].corr()\n",
    "unrelevant_features['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 5), dpi=200)\n",
    "sns.heatmap(unrelevant_features, annot=True, linewidths=0.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like the `Fare` actually have a positive relationship with `Survived`, not by much but worth noticing. Let's plot a box plot to visualize this clearer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Fare affect Passengers' Survival Status?\"},\"boxmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.box(\n",
    "    data_frame=train,\n",
    "    x=\"Survived\",\n",
    "    y=\"Fare\",\n",
    "    title=\"Does Fare affect Passengers' Survival Status?\",\n",
    ")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there is a segregation between rich and poor people. For example, the number of deceased passengers from third class is a lot greater than that of first class and second class. Or a lot of passengers who purchased higher fare ticket survived the event."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handle Missing Data <a id='handle-na'></a>\n",
    "[Back to top](#top)\n",
    "\n",
    "Let's take a look back our data to see which columns contain missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `Embarked` column\n",
    "First, I want to drop the 2 missing values of the `Embarked` column since they only account for 0.2 percent of the entire data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.dropna(subset=['Embarked'])\n",
    "train.isna().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `Age` column\n",
    "Next is the `Age` column. I want to look at the statistics for this column to strengthen my decision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    712.000000\n",
       "mean      29.642093\n",
       "std       14.492933\n",
       "min        0.420000\n",
       "25%       20.000000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
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   ],
   "source": [
    "fig = px.histogram(data_frame=train, x=\"Age\", marginal=\"box\")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean and the median of `Age` column are pretty close together. I'm not done here. I want to see which class those passengers with missing age values are from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Razi, Mr. Raihed</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2629</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "5              6         0       3                          Moran, Mr. James   \n",
       "17            18         1       2              Williams, Mr. Charles Eugene   \n",
       "19            20         1       3                   Masselmani, Mrs. Fatima   \n",
       "26            27         0       3                   Emir, Mr. Farred Chehab   \n",
       "28            29         1       3             O'Dwyer, Miss. Ellen \"Nellie\"   \n",
       "..           ...       ...     ...                                       ...   \n",
       "859          860         0       3                          Razi, Mr. Raihed   \n",
       "863          864         0       3         Sage, Miss. Dorothy Edith \"Dolly\"   \n",
       "868          869         0       3               van Melkebeke, Mr. Philemon   \n",
       "878          879         0       3                        Laleff, Mr. Kristo   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "\n",
       "        Sex  Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "5      male  NaN      0      0      330877   8.4583   NaN        Q  \n",
       "17     male  NaN      0      0      244373  13.0000   NaN        S  \n",
       "19   female  NaN      0      0        2649   7.2250   NaN        C  \n",
       "26     male  NaN      0      0        2631   7.2250   NaN        C  \n",
       "28   female  NaN      0      0      330959   7.8792   NaN        Q  \n",
       "..      ...  ...    ...    ...         ...      ...   ...      ...  \n",
       "859    male  NaN      0      0        2629   7.2292   NaN        C  \n",
       "863  female  NaN      8      2    CA. 2343  69.5500   NaN        S  \n",
       "868    male  NaN      0      0      345777   9.5000   NaN        S  \n",
       "878    male  NaN      0      0      349217   7.8958   NaN        S  \n",
       "888  female  NaN      1      2  W./C. 6607  23.4500   NaN        S  \n",
       "\n",
       "[177 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['Age'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(\n",
    "    data_frame=train[train[\"Age\"].isna()],\n",
    "    x=\"Pclass\",\n",
    "    title=\"Numbers of Passengers with Missing Age Values per Class\",\n",
    ")\n",
    "fig.update_layout(xaxis={\"categoryorder\": \"category ascending\", \"type\": \"category\"})\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passengers with missing age values are mostly from third class. I want to fill in those missing values with the age median of the corresponding class. \n",
    "\n",
    "Meaning:\n",
    "* Passengers with missing age values from first class will be filled with the age median of first class.\n",
    "* Passengers with missing age values from second class will be filled with the age median of second class.\n",
    "* Passengers with missing age values from third class will be filled with the age median of third class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    37.0\n",
       "2    29.0\n",
       "3    24.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_median_pclass = train.groupby('Pclass').median()['Age']\n",
    "age_median_pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.box(data_frame=train, x=\"Pclass\", y=\"Age\", title=\"Age Distribution per Class before Filling Missing Values\")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So:\n",
    "* Missing age values of passengers from first class will be filled with 37.\n",
    "* Missing age values of passengers from second class will be filled with 29.\n",
    "* Missing age values of passengers from third class will be filled with 24.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_age(pclass, age):\n",
    "    if np.isnan(age):\n",
    "        return age_median_pclass[pclass]\n",
    "    else:\n",
    "        return age\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Age\"] = train.apply(lambda table: fill_age(table[\"Pclass\"], table[\"Age\"]), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.box(data_frame=train, x=\"Pclass\", y=\"Age\", title=\"Age Distribution per Class after Filling Missing Values\")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `Cabin` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "583    A10\n",
       "475    A14\n",
       "556    A16\n",
       "284    A19\n",
       "599    A20\n",
       "      ... \n",
       "884    NaN\n",
       "885    NaN\n",
       "886    NaN\n",
       "888    NaN\n",
       "890    NaN\n",
       "Name: Cabin, Length: 889, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Cabin'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(x=train[\"Cabin\"].sort_values(), title=\"Cabin Value Counts\")\n",
    "fig.update_layout(xaxis_title=\"Unique Cabin Values\")\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I notice the naming convention is a letter followed by two or three digits. I also notice there are some strange cabin name entries such as `B57 B59 B63 B66`, `C23 C25 C27`, `D10 D12`, `C62 C64`, etc. Those cabins were probably occupied by members of the same family."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>311</th>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Miss. Emily Borie</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.375</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>743</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Miss. Susan Parker \"Suzette\"</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.375</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                   Name  \\\n",
       "311          312         1       1             Ryerson, Miss. Emily Borie   \n",
       "742          743         1       1  Ryerson, Miss. Susan Parker \"Suzette\"   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket     Fare            Cabin Embarked  \n",
       "311  female  18.0      2      2  PC 17608  262.375  B57 B59 B63 B66        C  \n",
       "742  female  21.0      2      2  PC 17608  262.375  B57 B59 B63 B66        C  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"Cabin\"] == \"B57 B59 B63 B66\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Ryerson family had 5 members including 3 siblings and 2 parents. They were staying in 4 cabins: B57, B59 B63 and B66. Only two sisters survived, the other members were missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>97</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Greenfield, Mr. William Bertram</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17759</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>D10 D12</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                             Name   Sex  \\\n",
       "97           98         1       1  Greenfield, Mr. William Bertram  male   \n",
       "\n",
       "     Age  SibSp  Parch    Ticket     Fare    Cabin Embarked  \n",
       "97  23.0      0      1  PC 17759  63.3583  D10 D12        C  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['Cabin'] == 'D10 D12']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mr. William Bertram had one parent/child aboard the Titanic. He probalby stayed in one room and the other family member stayed in the other. Unfortunately, we don't know who the other family member is, probably that family member is the one that has `NaN` for Cabin number.\n",
    "\n",
    "I want to create another column called \"Cabin Class\" which contains the first letter of each value in the \"Cabin\" column. This way, I can treat the \"Cabin Class\" column as the high-level class and the \"Cabin\" column as the detailed subclass of the \"Cabin Class\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Cabin Class\"] = train[\"Cabin\"].map(lambda x: x[0], na_action=\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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       "    .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>Cabin Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C123</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>B42</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cabin Cabin Class\n",
       "0     NaN         NaN\n",
       "1     C85           C\n",
       "2     NaN         NaN\n",
       "3    C123           C\n",
       "4     NaN         NaN\n",
       "..    ...         ...\n",
       "886   NaN         NaN\n",
       "887   B42           B\n",
       "888   NaN         NaN\n",
       "889  C148           C\n",
       "890   NaN         NaN\n",
       "\n",
       "[889 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Cabin', 'Cabin Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Cabin Class'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the `NaN` in the \"Cabin Class\", I want to assign **U** to them (U stands for unknown cabin class)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Cabin Class'] = train['Cabin Class'].fillna('U')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['U', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Cabin Class'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's drop the \"Cabin\" column since it is not necessary anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop('Cabin', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "Cabin Class    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing <a id='preprocessing'></a>\n",
    "[Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <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>Embarked</th>\n",
       "      <th>Cabin Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>820</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Skoog, Master. Karl Thorsten</td>\n",
       "      <td>male</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>347088</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>542</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Miss. Ingeborg Constanzia</td>\n",
       "      <td>female</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>577</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Garside, Miss. Ethel</td>\n",
       "      <td>female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>243880</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Van Impe, Miss. Catharina</td>\n",
       "      <td>female</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>345773</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Honkanen, Miss. Eliina</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101283</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>730</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ilmakangas, Miss. Pieta Sofia</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101271</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andreasson, Mr. Paul Edvin</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347466</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>516</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Walker, Mr. William Anderson</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36967</td>\n",
       "      <td>34.0208</td>\n",
       "      <td>S</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>680</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>West, Miss. Constance Mirium</td>\n",
       "      <td>female</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 34651</td>\n",
       "      <td>27.7500</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                  Name  \\\n",
       "819          820         0       3          Skoog, Master. Karl Thorsten   \n",
       "541          542         0       3  Andersson, Miss. Ingeborg Constanzia   \n",
       "576          577         1       2                  Garside, Miss. Ethel   \n",
       "419          420         0       3             Van Impe, Miss. Catharina   \n",
       "216          217         1       3                Honkanen, Miss. Eliina   \n",
       "729          730         0       3         Ilmakangas, Miss. Pieta Sofia   \n",
       "91            92         0       3            Andreasson, Mr. Paul Edvin   \n",
       "515          516         0       1          Walker, Mr. William Anderson   \n",
       "679          680         1       1    Cardeza, Mr. Thomas Drake Martinez   \n",
       "58            59         1       2          West, Miss. Constance Mirium   \n",
       "\n",
       "        Sex   Age  SibSp  Parch            Ticket      Fare Embarked  \\\n",
       "819    male  10.0      3      2            347088   27.9000        S   \n",
       "541  female   9.0      4      2            347082   31.2750        S   \n",
       "576  female  34.0      0      0            243880   13.0000        S   \n",
       "419  female  10.0      0      2            345773   24.1500        S   \n",
       "216  female  27.0      0      0  STON/O2. 3101283    7.9250        S   \n",
       "729  female  25.0      1      0  STON/O2. 3101271    7.9250        S   \n",
       "91     male  20.0      0      0            347466    7.8542        S   \n",
       "515    male  47.0      0      0             36967   34.0208        S   \n",
       "679    male  36.0      0      1          PC 17755  512.3292        C   \n",
       "58   female   5.0      1      2        C.A. 34651   27.7500        S   \n",
       "\n",
       "    Cabin Class  \n",
       "819           U  \n",
       "541           U  \n",
       "576           U  \n",
       "419           U  \n",
       "216           U  \n",
       "729           U  \n",
       "91            U  \n",
       "515           D  \n",
       "679           B  \n",
       "58            U  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.sample(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.drop(\n",
    "    [\"PassengerId\", \"Survived\", \"Name\", \"Ticket\", \"SibSp\", \"Parch\"], axis=1\n",
    ")\n",
    "y_train = train[\"Survived\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'Cabin Class'], dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = [\"Sex\", \"Embarked\", \"Cabin Class\"]\n",
    "\n",
    "one_hot = OneHotEncoder(handle_unknown=\"ignore\", drop='first')\n",
    "column_transformer = ColumnTransformer(\n",
    "    [(\"one_hot\", one_hot, categorical_columns)], remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "X_train = column_transformer.fit_transform(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['one_hot__Sex_male', 'one_hot__Embarked_Q', 'one_hot__Embarked_S',\n",
       "       'one_hot__Cabin Class_B', 'one_hot__Cabin Class_C',\n",
       "       'one_hot__Cabin Class_D', 'one_hot__Cabin Class_E',\n",
       "       'one_hot__Cabin Class_F', 'one_hot__Cabin Class_G',\n",
       "       'one_hot__Cabin Class_T', 'one_hot__Cabin Class_U',\n",
       "       'remainder__Pclass', 'remainder__Age', 'remainder__Fare'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_transformer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Models <a id='build-models'></a>\n",
    "[Back to top](#top)\n",
    "\n",
    "* [Models' performance report with default hyperparameters](#default-report)\n",
    "* [RandomizedSearchCV](#randomizedsearchcv)\n",
    "* [GridSearchCV](#gridsearchcv)\n",
    "\n",
    "We are going to train 3 models and compare the results of them. The three models are SVC, RandomForestClassifier and LogisticRegression.\n",
    "\n",
    "First I'm going to build the models with the default hyperparameters and evaluate them using cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END  accuracy: (test=0.798) precision: (test=0.864) recall: (test=0.559) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.775) precision: (test=0.750) recall: (test=0.618) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.775) precision: (test=0.818) recall: (test=0.529) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.831) precision: (test=0.788) recall: (test=0.765) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.876) precision: (test=0.926) recall: (test=0.735) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.798) precision: (test=0.808) recall: (test=0.618) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.809) precision: (test=0.870) recall: (test=0.588) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.764) precision: (test=0.760) recall: (test=0.559) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.843) precision: (test=0.812) recall: (test=0.765) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.818) precision: (test=0.875) recall: (test=0.618) total time=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "svc = SVC()\n",
    "cv_svc = cross_validate(\n",
    "    svc, X_train, y_train, scoring=[\"accuracy\", \"recall\", \"precision\"], cv=10, verbose=3\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END  accuracy: (test=0.742) precision: (test=0.667) recall: (test=0.647) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.798) precision: (test=0.722) recall: (test=0.765) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.742) precision: (test=0.690) recall: (test=0.588) total time=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END  accuracy: (test=0.831) precision: (test=0.744) recall: (test=0.853) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.843) precision: (test=0.778) recall: (test=0.824) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.787) precision: (test=0.714) recall: (test=0.735) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.809) precision: (test=0.815) recall: (test=0.647) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.787) precision: (test=0.727) recall: (test=0.706) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.888) precision: (test=0.833) recall: (test=0.882) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.830) precision: (test=0.788) recall: (test=0.765) total time=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    1.0s finished\n"
     ]
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier()\n",
    "cv_rf_clf = cross_validate(\n",
    "    rf_clf, X_train, y_train, scoring=[\"accuracy\", \"recall\", \"precision\"], cv=10, verbose=3\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END  accuracy: (test=0.753) precision: (test=0.688) recall: (test=0.647) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.809) precision: (test=0.730) recall: (test=0.794) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.775) precision: (test=0.733) recall: (test=0.647) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.865) precision: (test=0.806) recall: (test=0.853) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.764) precision: (test=0.667) recall: (test=0.765) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.809) precision: (test=0.774) recall: (test=0.706) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.775) precision: (test=0.733) recall: (test=0.647) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.798) precision: (test=0.767) recall: (test=0.676) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.843) precision: (test=0.812) recall: (test=0.765) total time=   0.0s\n",
      "[CV] END  accuracy: (test=0.761) precision: (test=0.686) recall: (test=0.706) total time=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "log_reg = LogisticRegression()\n",
    "cv_log_reg = cross_validate(\n",
    "    log_reg, X_train, y_train, scoring=[\"accuracy\", \"recall\", \"precision\"], cv=10, verbose=3\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>test_recall</th>\n",
       "      <th>test_precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.007698</td>\n",
       "      <td>0.008616</td>\n",
       "      <td>0.808784</td>\n",
       "      <td>0.635294</td>\n",
       "      <td>0.827038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.009253</td>\n",
       "      <td>0.007539</td>\n",
       "      <td>0.034665</td>\n",
       "      <td>0.087908</td>\n",
       "      <td>0.055631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.764045</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.780899</td>\n",
       "      <td>0.566176</td>\n",
       "      <td>0.792832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.002540</td>\n",
       "      <td>0.009812</td>\n",
       "      <td>0.803371</td>\n",
       "      <td>0.617647</td>\n",
       "      <td>0.815341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.015653</td>\n",
       "      <td>0.015626</td>\n",
       "      <td>0.828141</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.868083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.023725</td>\n",
       "      <td>0.015654</td>\n",
       "      <td>0.876404</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.925926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fit_time  score_time  test_accuracy  test_recall  test_precision\n",
       "count  10.000000   10.000000      10.000000    10.000000       10.000000\n",
       "mean    0.007698    0.008616       0.808784     0.635294        0.827038\n",
       "std     0.009253    0.007539       0.034665     0.087908        0.055631\n",
       "min     0.000000    0.000000       0.764045     0.529412        0.750000\n",
       "25%     0.000000    0.000999       0.780899     0.566176        0.792832\n",
       "50%     0.002540    0.009812       0.803371     0.617647        0.815341\n",
       "75%     0.015653    0.015626       0.828141     0.705882        0.868083\n",
       "max     0.023725    0.015654       0.876404     0.764706        0.925926"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cv_svc = pd.DataFrame(cv_svc)\n",
    "df_cv_svc.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>test_recall</th>\n",
       "      <th>test_precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.100946</td>\n",
       "      <td>0.006636</td>\n",
       "      <td>0.805426</td>\n",
       "      <td>0.741176</td>\n",
       "      <td>0.747750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.006408</td>\n",
       "      <td>0.007184</td>\n",
       "      <td>0.045153</td>\n",
       "      <td>0.095858</td>\n",
       "      <td>0.054254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.092895</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.741573</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.097657</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>0.661765</td>\n",
       "      <td>0.716270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.099276</td>\n",
       "      <td>0.004572</td>\n",
       "      <td>0.803371</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.735431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.103921</td>\n",
       "      <td>0.014298</td>\n",
       "      <td>0.830982</td>\n",
       "      <td>0.808824</td>\n",
       "      <td>0.785354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.112874</td>\n",
       "      <td>0.015649</td>\n",
       "      <td>0.887640</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fit_time  score_time  test_accuracy  test_recall  test_precision\n",
       "count  10.000000   10.000000      10.000000    10.000000       10.000000\n",
       "mean    0.100946    0.006636       0.805426     0.741176        0.747750\n",
       "std     0.006408    0.007184       0.045153     0.095858        0.054254\n",
       "min     0.092895    0.000000       0.741573     0.588235        0.666667\n",
       "25%     0.097657    0.000000       0.786517     0.661765        0.716270\n",
       "50%     0.099276    0.004572       0.803371     0.750000        0.735431\n",
       "75%     0.103921    0.014298       0.830982     0.808824        0.785354\n",
       "max     0.112874    0.015649       0.887640     0.882353        0.833333"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cv_rf_clf = pd.DataFrame(cv_rf_clf)\n",
    "df_cv_rf_clf.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>test_recall</th>\n",
       "      <th>test_precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.007711</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.795237</td>\n",
       "      <td>0.720588</td>\n",
       "      <td>0.739519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.002047</td>\n",
       "      <td>0.000675</td>\n",
       "      <td>0.036885</td>\n",
       "      <td>0.071036</td>\n",
       "      <td>0.050230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.003196</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.752809</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.006983</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.766854</td>\n",
       "      <td>0.654412</td>\n",
       "      <td>0.698057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.008128</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.008760</td>\n",
       "      <td>0.001999</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.772312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.011000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.865169</td>\n",
       "      <td>0.852941</td>\n",
       "      <td>0.812500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fit_time  score_time  test_accuracy  test_recall  test_precision\n",
       "count  10.000000   10.000000      10.000000    10.000000       10.000000\n",
       "mean    0.007711    0.001300       0.795237     0.720588        0.739519\n",
       "std     0.002047    0.000675       0.036885     0.071036        0.050230\n",
       "min     0.003196    0.000000       0.752809     0.647059        0.666667\n",
       "25%     0.006983    0.001000       0.766854     0.654412        0.698057\n",
       "50%     0.008128    0.001000       0.786517     0.705882        0.733333\n",
       "75%     0.008760    0.001999       0.808989     0.764706        0.772312\n",
       "max     0.011000    0.002000       0.865169     0.852941        0.812500"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cv_log_reg = pd.DataFrame(cv_log_reg)\n",
    "df_cv_log_reg.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models' performance report with default hyperparameters <a id='default-report'></a>\n",
    "\n",
    "| | Mean Accuracy | Mean Recall | Mean Precision |\n",
    "|:-|-----:|------------------------:|--------------------:|\n",
    "| SVC | 0.808784 | 0.635294 | 0.827038 |\n",
    "| RandomForestClassifier | 0.816662 | 0.747059 | 0.768144 |\n",
    "| LogisticRegression | 0.795237 | 0.720588 | 0.739519 |\n",
    "\n",
    "It looks like RandomForestClassifier achieves highest accuracy and recall score. Meanwhile SVC achieves highest precision score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters tuning using RandomizedSearchCV <a id='randomizedsearchcv'></a>\n",
    "I want to use RandomizedSearchCV to narrow down the hyperparameters and then perform GridSearchCV to refine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# svc_random_grid = {\n",
    "#     \"C\": np.logspace(-3, 3, 10),\n",
    "#     \"kernel\": [\"linear\", \"poly\", \"rbf\", \"sigmoid\"],\n",
    "#     \"degree\": np.arange(2, 5),\n",
    "#     \"max_iter\": np.linspace(100, 5000, 11),\n",
    "# }\n",
    "\n",
    "# rs_svc = RandomizedSearchCV(\n",
    "#     SVC(),\n",
    "#     param_distributions=svc_random_grid,\n",
    "#     n_iter=500,\n",
    "#     cv=10,\n",
    "#     verbose=1,\n",
    "#     scoring=\"accuracy\",\n",
    "# )\n",
    "# rs_svc.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rf_clf_random_grid = {\n",
    "#     \"n_estimators\": np.arange(100, 1000, 100),\n",
    "#     \"max_features\": [\"sqrt\", \"log2\"],\n",
    "# }\n",
    "\n",
    "# rs_rf_clf = RandomizedSearchCV(\n",
    "#     RandomForestClassifier(),\n",
    "#     param_distributions=rf_clf_random_grid,\n",
    "#     n_iter=100,\n",
    "#     cv=10,\n",
    "#     verbose=1,\n",
    "#     scoring=\"accuracy\",\n",
    "# )\n",
    "# rs_rf_clf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log_reg_random_grid = {\n",
    "#     \"penalty\": [\"l1\", \"l2\", \"elasticnet\"],\n",
    "#     \"C\": np.logspace(-3, 3, 10),\n",
    "#     \"solver\": [\"lbfgs\", \"saga\", \"liblinear\"],\n",
    "#     \"max_iter\": np.linspace(100, 5000, 11),\n",
    "#     \"l1_ratio\": np.logspace(-3, 1, 10),\n",
    "# }\n",
    "\n",
    "# rs_log_reg = RandomizedSearchCV(\n",
    "#     LogisticRegression(),\n",
    "#     param_distributions=log_reg_random_grid,\n",
    "#     n_iter=500,\n",
    "#     cv=10,\n",
    "#     verbose=1,\n",
    "#     scoring=\"accuracy\",\n",
    "# )\n",
    "# rs_log_reg.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_best_performance(model, name: str):\n",
    "    print(f'Model\\'s name: {name}')\n",
    "    print('----------')\n",
    "    print(f\"Best score: {model.best_score_}\")\n",
    "    print(f\"Best Params: {model.best_params_}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print_best_performance(rs_svc, \"SVC\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print_best_performance(rs_rf_clf, \"RandomForestClassifier\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print_best_performance(rs_log_reg, \"LogisticRegression\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearchCV <a id='gridsearchcv'></a>\n",
    "Since the RandomForestClassifier does not improve much after RandomizedSearchCV, I will only use GridSearchCV on SVC and LogisticRegression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 11 candidates, totalling 110 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=500).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=510).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=520).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=530).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=540).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=550).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=560).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=570).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=580).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=600).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n",
      "d:\\ml_datascience\\titanic-dataset\\env\\lib\\site-packages\\sklearn\\svm\\_base.py:284: ConvergenceWarning:\n",
      "\n",
      "Solver terminated early (max_iter=590).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': [215.44346900318823], 'kernel': ['rbf'],\n",
       "                         'max_iter': array([500., 510., 520., 530., 540., 550., 560., 570., 580., 590., 600.])},\n",
       "             refit='accuracy', scoring=['accuracy', 'recall', 'precision'],\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_gs_grid = {\n",
    "    \"kernel\": [\"rbf\"],\n",
    "    \"C\": [215.44346900318823],\n",
    "    \"max_iter\": np.linspace(500, 600, 11),\n",
    "}\n",
    "\n",
    "gs_svc = GridSearchCV(\n",
    "    SVC(),\n",
    "    svc_gs_grid,\n",
    "    scoring=[\"accuracy\", \"recall\", \"precision\"],\n",
    "    refit=\"accuracy\",\n",
    "    cv=10,\n",
    "    verbose=1,\n",
    ")\n",
    "gs_svc.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 11 candidates, totalling 110 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.1], 'l1_ratio': [0.007742636826811269],\n",
       "                         'max_iter': array([1000.,  920.,  840.,  760.,  680.,  600.,  520.,  440.,  360.,\n",
       "        280.,  200.]),\n",
       "                         'penalty': ['elasticnet'], 'solver': ['saga']},\n",
       "             refit='accuracy', scoring=['accuracy', 'recall', 'precision'],\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg_gs_grid = {\n",
    "    \"solver\": [\"saga\"],\n",
    "    \"penalty\": [\"elasticnet\"],\n",
    "    \"max_iter\": np.linspace(1000, 200, 11),\n",
    "    \"C\": [0.1],\n",
    "    \"l1_ratio\": [0.007742636826811269],\n",
    "}\n",
    "\n",
    "gs_log_reg = GridSearchCV(\n",
    "    LogisticRegression(),\n",
    "    log_reg_gs_grid,\n",
    "    scoring=[\"accuracy\", \"recall\", \"precision\"],\n",
    "    refit=\"accuracy\",\n",
    "    cv=10,\n",
    "    verbose=1,\n",
    ")\n",
    "gs_log_reg.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model's name: GridSearchCV_SVC\n",
      "----------\n",
      "Best score: 0.8256767109295199\n",
      "Best Params: {'C': 215.44346900318823, 'kernel': 'rbf', 'max_iter': 590.0}\n"
     ]
    }
   ],
   "source": [
    "print_best_performance(gs_svc, \"GridSearchCV_SVC\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model's name: GridSearchCV_LogisticRegression\n",
      "----------\n",
      "Best score: 0.8008810010214505\n",
      "Best Params: {'C': 0.1, 'l1_ratio': 0.007742636826811269, 'max_iter': 1000.0, 'penalty': 'elasticnet', 'solver': 'saga'}\n"
     ]
    }
   ],
   "source": [
    "print_best_performance(gs_log_reg, \"GridSearchCV_LogisticRegression\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load test.csv <a id='load-test'></a>\n",
    "[Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('./data/test.csv')\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the model to predict the test set, I have to change test set to look like to train set, namely create a new column called Cabin Class and drop the Cabin column.\n",
    "\n",
    "But first, I want to fill missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill missing Age value\n",
    "I will fill the missing Age value with the median age of each class from the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Age\"] = test.apply(lambda table: fill_age(table[\"Pclass\"], table[\"Age\"]), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Cabin Class column + assign 'U' for NA class + drop the Cabin column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Cabin Class\"] = test[\"Cabin\"].map(lambda x: x[0], na_action=\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Cabin Class\"] = test[\"Cabin Class\"].fillna(\"U\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['U', 'B', 'E', 'A', 'C', 'D', 'F', 'G'], dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Cabin Class'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop('Cabin', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           1\n",
       "Embarked       0\n",
       "Cabin Class    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Fare column has 1 missing value, I decide to drop this value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "Cabin Class    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = test.dropna(axis=0)\n",
    "test.isna().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare The Test Data for Prediction <a id='prepare-test'></a>\n",
    "[Back to top](#top)\n",
    "\n",
    "Since the models have learnt the train data with some columns dropped, we have to make sure to drop those same columns on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test.drop(\n",
    "    [\"PassengerId\", \"Name\", \"Ticket\", \"SibSp\", \"Parch\"], axis=1\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>C</td>\n",
       "      <td>U</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex   Age      Fare Embarked Cabin Class\n",
       "0         3    male  34.5    7.8292        Q           U\n",
       "1         3  female  47.0    7.0000        S           U\n",
       "2         2    male  62.0    9.6875        Q           U\n",
       "3         3    male  27.0    8.6625        S           U\n",
       "4         3  female  22.0   12.2875        S           U\n",
       "..      ...     ...   ...       ...      ...         ...\n",
       "413       3    male  24.0    8.0500        S           U\n",
       "414       1  female  39.0  108.9000        C           C\n",
       "415       3    male  38.5    7.2500        S           U\n",
       "416       3    male  24.0    8.0500        S           U\n",
       "417       3    male  24.0   22.3583        C           U\n",
       "\n",
       "[417 rows x 6 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we perform one-hot encoding and standard scaling before feeding it to our models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = column_transformer.transform(X_test)\n",
    "X_test = scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make prediction\n",
    "I'm going to use the SVC model to make predictions since this models achieve highest accuracy score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gs_svc.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "      dtype=int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         0\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         1\n",
       "\n",
       "[417 rows x 2 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame({\"PassengerId\": test[\"PassengerId\"], \"Survived\": y_pred})\n",
    "result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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