notebooks/05-logistic-regression-titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression \n",
    "\n",
    "- Data should be free from missing values\n",
    "- Predictant variable is binary or ordinal i.e., categorical variable with ordered values\n",
    "- All predictors are independent of each other\n",
    "- Sufficient observation (at least 50 per predictor) for reliable output\n",
    "\n",
    "We will be using [Titanic dataset](https://www.kaggle.com/c/titanic/data) to predict the survival of passengers. \n",
    "\n",
    "**Data description:**  \n",
    "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: number of siblings/spouses aboard the Titanic  \n",
    "parch: number of parents/children aboard the Titanic  \n",
    "ticket: Ticket number  \n",
    "fare: Passenger fare  \n",
    "cabin: Cabin number  \n",
    "embarked: Port of Embarkation, C = Cherbourg, Q = Queenstown, S = Southampton  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split, cross_val_predict\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import missingno\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading our data\n",
    "data = pd.read_csv(\"../datasets/titanic/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many missing values are there in our dataset?\n",
    "missingno.matrix(data, figsize = (30,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Survived\", data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As there are too many missing values in Cabin column, we decide to drop this \n",
    "# column \n",
    "data.drop(columns=[\"Name\", \"Ticket\", \"Cabin\"], inplace=True)\n",
    "\n",
    "# Alternative way to drop data\n",
    "# data = data.drop([\"Name\", \"Ticket\", \"Cabin\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0            1         0       3    male  22.0      1      0   7.2500        S\n",
       "1            2         1       1  female  38.0      1      0  71.2833        C\n",
       "2            3         1       3  female  26.0      0      0   7.9250        S\n",
       "3            4         1       1  female  35.0      1      0  53.1000        S\n",
       "4            5         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    678\n",
       "1    118\n",
       "2     80\n",
       "5      5\n",
       "3      5\n",
       "4      4\n",
       "6      1\n",
       "Name: Parch, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Parch\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Treating missing age values\n",
    "sns.boxplot(x=\"Parch\", y=\"Age\", data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>445.255162</td>\n",
       "      <td>0.343658</td>\n",
       "      <td>2.321534</td>\n",
       "      <td>32.178503</td>\n",
       "      <td>0.237463</td>\n",
       "      <td>25.586774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>465.110169</td>\n",
       "      <td>0.550847</td>\n",
       "      <td>2.203390</td>\n",
       "      <td>24.422000</td>\n",
       "      <td>1.084746</td>\n",
       "      <td>46.778180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>416.662500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.275000</td>\n",
       "      <td>17.216912</td>\n",
       "      <td>2.062500</td>\n",
       "      <td>64.337604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>579.200000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>33.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.951660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>384.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>84.968750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>435.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.200000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>32.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>679.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>46.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId  Survived    Pclass        Age     SibSp       Fare\n",
       "Parch                                                                 \n",
       "0       445.255162  0.343658  2.321534  32.178503  0.237463  25.586774\n",
       "1       465.110169  0.550847  2.203390  24.422000  1.084746  46.778180\n",
       "2       416.662500  0.500000  2.275000  17.216912  2.062500  64.337604\n",
       "3       579.200000  0.600000  2.600000  33.200000  1.000000  25.951660\n",
       "4       384.000000  0.000000  2.500000  44.500000  0.750000  84.968750\n",
       "5       435.200000  0.200000  3.000000  39.200000  0.600000  32.550000\n",
       "6       679.000000  0.000000  3.000000  43.000000  1.000000  46.900000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_grp = data.groupby(data[\"Parch\"]).mean(); data_grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will replace the missing age values by average age based on the Parch values\n",
    "\n",
    "def replace_age(cols):\n",
    "    age = cols[0]\n",
    "    parch = cols[1]\n",
    "    \n",
    "    if pd.isna(age):\n",
    "        if parch == 0:\n",
    "            return 32\n",
    "        elif parch == 1:\n",
    "            return 24\n",
    "        elif parch == 2:\n",
    "            return 17\n",
    "        elif parch == 3:\n",
    "            return 33\n",
    "        elif parch == 4:\n",
    "            return 45\n",
    "        elif parch == 5:\n",
    "            return 39\n",
    "        else: \n",
    "            return 43\n",
    "    else:\n",
    "        return age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age'] = data[['Age', 'Parch']].apply(replace_age, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Fare           0\n",
       "Embarked       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 889 entries, 0 to 888\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  889 non-null    int64  \n",
      " 1   Survived     889 non-null    int64  \n",
      " 2   Pclass       889 non-null    int64  \n",
      " 3   Sex          889 non-null    object \n",
      " 4   Age          889 non-null    float64\n",
      " 5   SibSp        889 non-null    int64  \n",
      " 6   Parch        889 non-null    int64  \n",
      " 7   Fare         889 non-null    float64\n",
      " 8   Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# remove the missing Embarked rows and reset the index \n",
    "data.dropna(inplace=True)\n",
    "data.reset_index(inplace=True, drop=True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting categorical variables to dummy indicators\n",
    "# For example, for our sex category we want 0 or 1 instead of male or female\n",
    "le = LabelEncoder()\n",
    "\n",
    "gender_encoded = le.fit_transform(data['Sex'])\n",
    "gender_encoded[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0            1         0       3    male  22.0      1      0   7.2500        S\n",
       "1            2         1       1  female  38.0      1      0  71.2833        C\n",
       "2            3         1       3  female  26.0      0      0   7.9250        S\n",
       "3            4         1       1  female  35.0      1      0  53.1000        S\n",
       "4            5         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male\n",
       "0     1\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So 1 indicates male and 0 indicates female\n",
    "# Create a new dataframe\n",
    "gender_df = pd.DataFrame(gender_encoded, columns=[\"male\"])\n",
    "gender_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do the same of embarked variable\n",
    "embarked_encoded = le.fit_transform(data['Embarked'])\n",
    "embarked_df = pd.DataFrame(embarked_encoded, columns=[\"embarked\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    644\n",
       "0    168\n",
       "1     77\n",
       "Name: embarked, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked_df['embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     C    Q    S\n",
       "0  0.0  0.0  1.0\n",
       "1  1.0  0.0  0.0\n",
       "2  0.0  0.0  1.0\n",
       "3  0.0  0.0  1.0\n",
       "4  0.0  0.0  1.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C = Cherbourg = 0, Q = Queenstown = 1, S = Southampton = 2\n",
    "# We need to create separate binary columns for each of Embarked variable\n",
    "\n",
    "ohe = OneHotEncoder()\n",
    "embarked_binary = ohe.fit_transform(embarked_encoded.reshape(-1, 1)).toarray()\n",
    "embarked_df = pd.DataFrame(embarked_binary, columns=['C', 'Q', 'S'])\n",
    "embarked_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0            1         0       3  22.0      1      0   7.2500\n",
       "1            2         1       1  38.0      1      0  71.2833\n",
       "2            3         1       3  26.0      0      0   7.9250\n",
       "3            4         1       1  35.0      1      0  53.1000\n",
       "4            5         0       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the sex and embarked colums from original dataframe and add the newly \n",
    "# created binary columns\n",
    "data.drop(['Sex', 'Embarked'], axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Age  SibSp  Parch  Fare  male  C  Q  S\n",
       "0            1         0       3   22      1      0     7     1  0  0  1\n",
       "1            2         1       1   38      1      0    71     0  1  0  0\n",
       "2            3         1       3   26      0      0     7     0  0  0  1\n",
       "3            4         1       1   35      1      0    53     0  0  0  1\n",
       "4            5         0       3   35      0      0     8     1  0  0  1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dummy = pd.concat([data, gender_df, embarked_df], axis=1, \\\n",
    "                       verify_integrity=True).astype(int)\n",
    "data_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking for independence among variables \n",
    "sns.heatmap(data_dummy.corr())\n",
    "plt.colorbar\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Age  SibSp  Parch  male  C  Q  S\n",
       "0            1         0   22      1      0     1  0  0  1\n",
       "1            2         1   38      1      0     0  1  0  0\n",
       "2            3         1   26      0      0     0  0  0  1\n",
       "3            4         1   35      1      0     0  0  0  1\n",
       "4            5         0   35      0      0     1  0  0  1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As our model assumes independent variables we drop some of the dependent variables\n",
    "data_dummy.drop(['Fare', 'Pclass'], axis=1, inplace=True)\n",
    "data_dummy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split train test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, x_test, y, y_test = train_test_split(data_dummy.drop(['Survived'],axis=1),\\\n",
    "                                       data_dummy['Survived'], test_size=0.2,\n",
    "                                        random_state=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(711, 8) (178, 8) (711,) (178,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape, x_test.shape, y.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Deploy model\n",
    "lr = LogisticRegression(solver='liblinear')\n",
    "lr.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.88      0.85       109\n",
      "           1       0.79      0.71      0.75        69\n",
      "\n",
      "    accuracy                           0.81       178\n",
      "   macro avg       0.81      0.80      0.80       178\n",
      "weighted avg       0.81      0.81      0.81       178\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[377,  63],\n",
       "       [ 91, 180]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation and confusion matrix\n",
    "y_train_pred = cross_val_predict(lr, x, y, cv=5)\n",
    "confusion_matrix(y, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7407407407407407"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    866\n",
       "Survived         1\n",
       "Age             42\n",
       "SibSp            0\n",
       "Parch            0\n",
       "male             0\n",
       "C                0\n",
       "Q                0\n",
       "S                1\n",
       "Name: 863, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dummy.iloc[863]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Age  SibSp  Parch  male  C  Q  S\n",
       "863          866         1   42      0      0     0  0  0  1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dummy[863:864]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "test_passenger = np.array([866, 35, 1, 1, 0, 0, 0, 1]).reshape(1, -1)\n",
    "print(lr.predict(test_passenger))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.32341569 0.67658431]]\n"
     ]
    }
   ],
   "source": [
    "print(lr.predict_proba(test_passenger))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}