exercise16_bayesian-titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise No. 16 \n",
    "## Titanic Dataset with Bayesian Statistics\n",
    "#### 2019 June 25 | Cameron.Cn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.naive_bayes import GaussianNB   #import Gaussian Bayes modeling function\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.naive_bayes import BernoulliNB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0       1         1                    Allen, Miss. Elisabeth Walton  female   \n",
       "1       1         1                   Allison, Master. Hudson Trevor    male   \n",
       "2       1         0                     Allison, Miss. Helen Loraine  female   \n",
       "3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000      0      0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167      1      2  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000      1      2  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000      1      2  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc = './Module_0/datasets/titanic.xls'\n",
    "\n",
    "df = pd.read_excel(loc)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass          0\n",
       "survived        0\n",
       "name            0\n",
       "sex             0\n",
       "age           263\n",
       "sibsp           0\n",
       "parch           0\n",
       "ticket          0\n",
       "fare            1\n",
       "cabin        1014\n",
       "embarked        2\n",
       "boat          823\n",
       "body         1188\n",
       "home.dest     564\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Baumann, Mr. John D</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17318</td>\n",
       "      <td>25.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bradley, Mr. George (\"George Arthur Brayton\")</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111427</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Brewe, Dr. Arthur Jackson</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112379</td>\n",
       "      <td>39.6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cairns, Mr. Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113798</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev...</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17770</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pclass  survived                                               name  \\\n",
       "15       1         0                                Baumann, Mr. John D   \n",
       "37       1         1      Bradley, Mr. George (\"George Arthur Brayton\")   \n",
       "40       1         0                          Brewe, Dr. Arthur Jackson   \n",
       "46       1         0                              Cairns, Mr. Alexander   \n",
       "59       1         1  Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev...   \n",
       "\n",
       "       sex  age  sibsp  parch    ticket     fare cabin embarked boat  body  \\\n",
       "15    male  NaN      0      0  PC 17318  25.9250   NaN        S  NaN   NaN   \n",
       "37    male  NaN      0      0    111427  26.5500   NaN        S    9   NaN   \n",
       "40    male  NaN      0      0    112379  39.6000   NaN        C  NaN   NaN   \n",
       "46    male  NaN      0      0    113798  31.0000   NaN        S  NaN   NaN   \n",
       "59  female  NaN      0      0     17770  27.7208   NaN        C    5   NaN   \n",
       "\n",
       "           home.dest  \n",
       "15      New York, NY  \n",
       "37   Los Angeles, CA  \n",
       "40  Philadelphia, PA  \n",
       "46               NaN  \n",
       "59      New York, NY  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_age = df.loc[df['age'].isnull()]\n",
    "missing_age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mal = list(missing_age.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived  sex     pclass\n",
       "0         female  1         35.200000\n",
       "                  2         34.090909\n",
       "                  3         23.418750\n",
       "          male    1         43.658163\n",
       "                  2         33.092593\n",
       "                  3         26.679598\n",
       "1         female  1         37.109375\n",
       "                  2         26.711051\n",
       "                  3         20.814815\n",
       "          male    1         36.168240\n",
       "                  2         17.449274\n",
       "                  3         22.436441\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['survived','sex','pclass'])['age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age'].fillna(df.groupby(['survived','sex','pclass'])['age'].transform('mean'), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Baumann, Mr. John D</td>\n",
       "      <td>male</td>\n",
       "      <td>43.658163</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17318</td>\n",
       "      <td>25.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bradley, Mr. George (\"George Arthur Brayton\")</td>\n",
       "      <td>male</td>\n",
       "      <td>36.168240</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111427</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Brewe, Dr. Arthur Jackson</td>\n",
       "      <td>male</td>\n",
       "      <td>43.658163</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112379</td>\n",
       "      <td>39.6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cairns, Mr. Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>43.658163</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113798</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev...</td>\n",
       "      <td>female</td>\n",
       "      <td>37.109375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17770</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pclass  survived                                               name  \\\n",
       "15       1         0                                Baumann, Mr. John D   \n",
       "37       1         1      Bradley, Mr. George (\"George Arthur Brayton\")   \n",
       "40       1         0                          Brewe, Dr. Arthur Jackson   \n",
       "46       1         0                              Cairns, Mr. Alexander   \n",
       "59       1         1  Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev...   \n",
       "\n",
       "       sex        age  sibsp  parch    ticket     fare cabin embarked boat  \\\n",
       "15    male  43.658163      0      0  PC 17318  25.9250   NaN        S  NaN   \n",
       "37    male  36.168240      0      0    111427  26.5500   NaN        S    9   \n",
       "40    male  43.658163      0      0    112379  39.6000   NaN        C  NaN   \n",
       "46    male  43.658163      0      0    113798  31.0000   NaN        S  NaN   \n",
       "59  female  37.109375      0      0     17770  27.7208   NaN        C    5   \n",
       "\n",
       "    body         home.dest  \n",
       "15   NaN      New York, NY  \n",
       "37   NaN   Los Angeles, CA  \n",
       "40   NaN  Philadelphia, PA  \n",
       "46   NaN               NaN  \n",
       "59   NaN      New York, NY  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[mal].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass          0\n",
       "survived        0\n",
       "name            0\n",
       "sex             0\n",
       "age             0\n",
       "sibsp           0\n",
       "parch           0\n",
       "ticket          0\n",
       "fare            1\n",
       "cabin        1014\n",
       "embarked        2\n",
       "boat          823\n",
       "body         1188\n",
       "home.dest     564\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cincinatti, OH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass  survived                                       name     sex  \\\n",
       "168       1         1                        Icard, Miss. Amelie  female   \n",
       "284       1         1  Stone, Mrs. George Nelson (Martha Evelyn)  female   \n",
       "\n",
       "      age  sibsp  parch  ticket  fare cabin embarked boat  body  \\\n",
       "168  38.0      0      0  113572  80.0   B28      NaN    6   NaN   \n",
       "284  62.0      0      0  113572  80.0   B28      NaN    6   NaN   \n",
       "\n",
       "          home.dest  \n",
       "168             NaN  \n",
       "284  Cincinatti, OH  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark = df.loc[df['embarked'].isnull()]\n",
    "embark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "embarkls = list(embark.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['embarked'].fillna('S', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>S</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>S</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cincinatti, OH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass  survived                                       name     sex  \\\n",
       "168       1         1                        Icard, Miss. Amelie  female   \n",
       "284       1         1  Stone, Mrs. George Nelson (Martha Evelyn)  female   \n",
       "\n",
       "      age  sibsp  parch  ticket  fare cabin embarked boat  body  \\\n",
       "168  38.0      0      0  113572  80.0   B28        S    6   NaN   \n",
       "284  62.0      0      0  113572  80.0   B28        S    6   NaN   \n",
       "\n",
       "          home.dest  \n",
       "168             NaN  \n",
       "284  Cincinatti, OH  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[embarkls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass          0\n",
       "survived        0\n",
       "name            0\n",
       "sex             0\n",
       "age             0\n",
       "sibsp           0\n",
       "parch           0\n",
       "ticket          0\n",
       "fare            1\n",
       "cabin        1014\n",
       "embarked        0\n",
       "boat          823\n",
       "body         1188\n",
       "home.dest     564\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived     sex      age  sibsp  parch embarked\n",
       "0       1         1  female  29.0000      0      0        S\n",
       "1       1         1    male   0.9167      1      2        S\n",
       "2       1         0  female   2.0000      1      2        S\n",
       "3       1         0    male  30.0000      1      2        S\n",
       "4       1         0  female  25.0000      1      2        S"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeldf = df.drop(['name','ticket','fare','cabin','boat','body','home.dest'], axis=1)\n",
    "modeldf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>2</th>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>3</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived     sex      age  sibsp  parch  pclass_1  pclass_2  pclass_3  \\\n",
       "0         1  female  29.0000      0      0         1         0         0   \n",
       "1         1    male   0.9167      1      2         1         0         0   \n",
       "2         0  female   2.0000      1      2         1         0         0   \n",
       "3         0    male  30.0000      1      2         1         0         0   \n",
       "4         0  female  25.0000      1      2         1         0         0   \n",
       "\n",
       "   embarked_C  embarked_Q  embarked_S  \n",
       "0           0           0           1  \n",
       "1           0           0           1  \n",
       "2           0           0           1  \n",
       "3           0           0           1  \n",
       "4           0           0           1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeldf = pd.get_dummies(data=modeldf, columns=['pclass','embarked'])\n",
    "modeldf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  sex      age  sibsp  parch  pclass_1  pclass_2  pclass_3  \\\n",
       "0         1    0  29.0000      0      0         1         0         0   \n",
       "1         1    1   0.9167      1      2         1         0         0   \n",
       "2         0    0   2.0000      1      2         1         0         0   \n",
       "3         0    1  30.0000      1      2         1         0         0   \n",
       "4         0    0  25.0000      1      2         1         0         0   \n",
       "\n",
       "   embarked_C  embarked_Q  embarked_S  \n",
       "0           0           0           1  \n",
       "1           0           0           1  \n",
       "2           0           0           1  \n",
       "3           0           0           1  \n",
       "4           0           0           1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeldf['sex'] = modeldf['sex'].map({'female':0, 'male':1})\n",
    "modeldf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>familyNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0000</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9167</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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0000</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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0000</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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  sex      age  pclass_1  pclass_2  pclass_3  embarked_C  \\\n",
       "0         1    0  29.0000         1         0         0           0   \n",
       "1         1    1   0.9167         1         0         0           0   \n",
       "2         0    0   2.0000         1         0         0           0   \n",
       "3         0    1  30.0000         1         0         0           0   \n",
       "4         0    0  25.0000         1         0         0           0   \n",
       "\n",
       "   embarked_Q  embarked_S  familyNum  \n",
       "0           0           1          0  \n",
       "1           0           1          3  \n",
       "2           0           1          3  \n",
       "3           0           1          3  \n",
       "4           0           1          3  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeldf['familyNum'] = modeldf['sibsp'] + modeldf['parch']\n",
    "modeldf.drop(['sibsp','parch'],axis=1, inplace=True)\n",
    "modeldf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>familyNum</th>\n",
       "      <th>TravelAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0000</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9167</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",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0000</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",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</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",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0000</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",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  sex      age  pclass_1  pclass_2  pclass_3  embarked_C  \\\n",
       "0         1    0  29.0000         1         0         0           0   \n",
       "1         1    1   0.9167         1         0         0           0   \n",
       "2         0    0   2.0000         1         0         0           0   \n",
       "3         0    1  30.0000         1         0         0           0   \n",
       "4         0    0  25.0000         1         0         0           0   \n",
       "\n",
       "   embarked_Q  embarked_S  familyNum  TravelAlone  \n",
       "0           0           1          0            1  \n",
       "1           0           1          3            0  \n",
       "2           0           1          3            0  \n",
       "3           0           1          3            0  \n",
       "4           0           1          3            0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeldf['TravelAlone'] = np.where((modeldf['familyNum'] > 0), 0, 1)\n",
    "# if someone is alone, '1', if they had family, '0'\n",
    "modeldf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survived       1309\n",
      "sex            1309\n",
      "age            1309\n",
      "pclass_1       1309\n",
      "pclass_2       1309\n",
      "pclass_3       1309\n",
      "embarked_C     1309\n",
      "embarked_Q     1309\n",
      "embarked_S     1309\n",
      "familyNum      1309\n",
      "TravelAlone    1309\n",
      "dtype: int64\n",
      "--------\n",
      "survived         int64\n",
      "sex              int64\n",
      "age            float64\n",
      "pclass_1         uint8\n",
      "pclass_2         uint8\n",
      "pclass_3         uint8\n",
      "embarked_C       uint8\n",
      "embarked_Q       uint8\n",
      "embarked_S       uint8\n",
      "familyNum        int64\n",
      "TravelAlone      int32\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(modeldf.count())\n",
    "print('--------')\n",
    "print(modeldf.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>familyNum</th>\n",
       "      <th>TravelAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.528693</td>\n",
       "      <td>-0.060032</td>\n",
       "      <td>0.279449</td>\n",
       "      <td>0.050790</td>\n",
       "      <td>-0.283428</td>\n",
       "      <td>0.182123</td>\n",
       "      <td>-0.016071</td>\n",
       "      <td>-0.150542</td>\n",
       "      <td>0.026876</td>\n",
       "      <td>-0.201719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.528693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.080752</td>\n",
       "      <td>-0.107371</td>\n",
       "      <td>-0.028862</td>\n",
       "      <td>0.116562</td>\n",
       "      <td>-0.066564</td>\n",
       "      <td>-0.088651</td>\n",
       "      <td>0.115193</td>\n",
       "      <td>-0.188583</td>\n",
       "      <td>0.284537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.060032</td>\n",
       "      <td>0.080752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.428501</td>\n",
       "      <td>0.005843</td>\n",
       "      <td>-0.375549</td>\n",
       "      <td>0.082706</td>\n",
       "      <td>-0.085716</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.206087</td>\n",
       "      <td>0.116266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass_1</th>\n",
       "      <td>0.279449</td>\n",
       "      <td>-0.107371</td>\n",
       "      <td>0.428501</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.296526</td>\n",
       "      <td>-0.622172</td>\n",
       "      <td>0.325722</td>\n",
       "      <td>-0.166101</td>\n",
       "      <td>-0.181800</td>\n",
       "      <td>-0.029656</td>\n",
       "      <td>-0.126551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass_2</th>\n",
       "      <td>0.050790</td>\n",
       "      <td>-0.028862</td>\n",
       "      <td>0.005843</td>\n",
       "      <td>-0.296526</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.563180</td>\n",
       "      <td>-0.134675</td>\n",
       "      <td>-0.121973</td>\n",
       "      <td>0.196532</td>\n",
       "      <td>-0.039976</td>\n",
       "      <td>-0.035075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass_3</th>\n",
       "      <td>-0.283428</td>\n",
       "      <td>0.116562</td>\n",
       "      <td>-0.375549</td>\n",
       "      <td>-0.622172</td>\n",
       "      <td>-0.563180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.171430</td>\n",
       "      <td>0.243706</td>\n",
       "      <td>-0.003805</td>\n",
       "      <td>0.058430</td>\n",
       "      <td>0.138250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embarked_C</th>\n",
       "      <td>0.182123</td>\n",
       "      <td>-0.066564</td>\n",
       "      <td>0.082706</td>\n",
       "      <td>0.325722</td>\n",
       "      <td>-0.134675</td>\n",
       "      <td>-0.171430</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.164166</td>\n",
       "      <td>-0.778262</td>\n",
       "      <td>-0.036553</td>\n",
       "      <td>-0.107874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embarked_Q</th>\n",
       "      <td>-0.016071</td>\n",
       "      <td>-0.088651</td>\n",
       "      <td>-0.085716</td>\n",
       "      <td>-0.166101</td>\n",
       "      <td>-0.121973</td>\n",
       "      <td>0.243706</td>\n",
       "      <td>-0.164166</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.491656</td>\n",
       "      <td>-0.087190</td>\n",
       "      <td>0.127214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embarked_S</th>\n",
       "      <td>-0.150542</td>\n",
       "      <td>0.115193</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.181800</td>\n",
       "      <td>0.196532</td>\n",
       "      <td>-0.003805</td>\n",
       "      <td>-0.778262</td>\n",
       "      <td>-0.491656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.087771</td>\n",
       "      <td>0.014246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>familyNum</th>\n",
       "      <td>0.026876</td>\n",
       "      <td>-0.188583</td>\n",
       "      <td>-0.206087</td>\n",
       "      <td>-0.029656</td>\n",
       "      <td>-0.039976</td>\n",
       "      <td>0.058430</td>\n",
       "      <td>-0.036553</td>\n",
       "      <td>-0.087190</td>\n",
       "      <td>0.087771</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.688864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TravelAlone</th>\n",
       "      <td>-0.201719</td>\n",
       "      <td>0.284537</td>\n",
       "      <td>0.116266</td>\n",
       "      <td>-0.126551</td>\n",
       "      <td>-0.035075</td>\n",
       "      <td>0.138250</td>\n",
       "      <td>-0.107874</td>\n",
       "      <td>0.127214</td>\n",
       "      <td>0.014246</td>\n",
       "      <td>-0.688864</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             survived       sex       age  pclass_1  pclass_2  pclass_3  \\\n",
       "survived     1.000000 -0.528693 -0.060032  0.279449  0.050790 -0.283428   \n",
       "sex         -0.528693  1.000000  0.080752 -0.107371 -0.028862  0.116562   \n",
       "age         -0.060032  0.080752  1.000000  0.428501  0.005843 -0.375549   \n",
       "pclass_1     0.279449 -0.107371  0.428501  1.000000 -0.296526 -0.622172   \n",
       "pclass_2     0.050790 -0.028862  0.005843 -0.296526  1.000000 -0.563180   \n",
       "pclass_3    -0.283428  0.116562 -0.375549 -0.622172 -0.563180  1.000000   \n",
       "embarked_C   0.182123 -0.066564  0.082706  0.325722 -0.134675 -0.171430   \n",
       "embarked_Q  -0.016071 -0.088651 -0.085716 -0.166101 -0.121973  0.243706   \n",
       "embarked_S  -0.150542  0.115193 -0.018446 -0.181800  0.196532 -0.003805   \n",
       "familyNum    0.026876 -0.188583 -0.206087 -0.029656 -0.039976  0.058430   \n",
       "TravelAlone -0.201719  0.284537  0.116266 -0.126551 -0.035075  0.138250   \n",
       "\n",
       "             embarked_C  embarked_Q  embarked_S  familyNum  TravelAlone  \n",
       "survived       0.182123   -0.016071   -0.150542   0.026876    -0.201719  \n",
       "sex           -0.066564   -0.088651    0.115193  -0.188583     0.284537  \n",
       "age            0.082706   -0.085716   -0.018446  -0.206087     0.116266  \n",
       "pclass_1       0.325722   -0.166101   -0.181800  -0.029656    -0.126551  \n",
       "pclass_2      -0.134675   -0.121973    0.196532  -0.039976    -0.035075  \n",
       "pclass_3      -0.171430    0.243706   -0.003805   0.058430     0.138250  \n",
       "embarked_C     1.000000   -0.164166   -0.778262  -0.036553    -0.107874  \n",
       "embarked_Q    -0.164166    1.000000   -0.491656  -0.087190     0.127214  \n",
       "embarked_S    -0.778262   -0.491656    1.000000   0.087771     0.014246  \n",
       "familyNum     -0.036553   -0.087190    0.087771   1.000000    -0.688864  \n",
       "TravelAlone   -0.107874    0.127214    0.014246  -0.688864     1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeldf.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1954b122d68>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.subplots(figsize=(8,6))\n",
    "sns.heatmap(modeldf.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>survived</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>pclass_2</th>\n",
       "      <th>pclass_3</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>familyNum</th>\n",
       "      <th>TravelAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0000</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9167</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",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0000</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",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</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",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0000</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",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  sex      age  pclass_1  pclass_2  pclass_3  embarked_C  \\\n",
       "0         1    0  29.0000         1         0         0           0   \n",
       "1         1    1   0.9167         1         0         0           0   \n",
       "2         0    0   2.0000         1         0         0           0   \n",
       "3         0    1  30.0000         1         0         0           0   \n",
       "4         0    0  25.0000         1         0         0           0   \n",
       "\n",
       "   embarked_Q  embarked_S  familyNum  TravelAlone  \n",
       "0           0           1          0            1  \n",
       "1           0           1          3            0  \n",
       "2           0           1          3            0  \n",
       "3           0           1          3            0  \n",
       "4           0           1          3            0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeldf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating our variables for the training data\n",
    "X = modeldf.drop('survived', axis=1)\n",
    "Y = modeldf['survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the syntax for this is ALWAYS THE SAME\n",
    "# X (Train, Test); Y (Train, Test)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=451)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Gaussian NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB() # to more easily call our Gaussian function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7573904179408767"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.score(X_train, Y_train)\n",
    "# only ~78% accurate! Not much better than a moderately educated guess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Predicted Non-Survival</th>\n",
       "      <th>Predicted Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True Non-Survival</th>\n",
       "      <td>142</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Survived</th>\n",
       "      <td>35</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Predicted Non-Survival  Predicted Survived\n",
       "True Non-Survival                     142                  63\n",
       "True Survived                          35                  88"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confMat = pd.DataFrame(\n",
    "    confusion_matrix(Y_test, Y_pred),\n",
    "    columns = ['Predicted Non-Survival','Predicted Survived'],\n",
    "    index = ['True Non-Survival','True Survived']\n",
    ")\n",
    "confMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    205\n",
       "1    123\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7012195121951219"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.75      0.81       223\n",
      "           1       0.59      0.75      0.66       105\n",
      "\n",
      "   micro avg       0.75      0.75      0.75       328\n",
      "   macro avg       0.73      0.75      0.73       328\n",
      "weighted avg       0.78      0.75      0.76       328\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### BernoulliNB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "bnb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bnb.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7584097859327217"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bnb.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred2 = bnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>Predicted Non-Survival</th>\n",
       "      <th>Predicted Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True Non-Survival</th>\n",
       "      <td>158</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Survived</th>\n",
       "      <td>38</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Predicted Non-Survival  Predicted Survived\n",
       "True Non-Survival                     158                  47\n",
       "True Survived                          38                  85"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confMat2 = pd.DataFrame(\n",
    "    confusion_matrix(Y_test, Y_pred2),\n",
    "    columns = ['Predicted Non-Survival','Predicted Survived'],\n",
    "    index = ['True Non-Survival','True Survived']\n",
    ")\n",
    "confMat2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7408536585365854"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bnb.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Rundown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The two methods weren't super accurate, and neither broke 80% accurate on test data.\n",
    "\n",
    "\n",
    "|[]|Bernoulli | Gaussian | Random State |\n",
    "| --- | --- | --- | --- |\n",
    "| Train | 76% | 78% | 129 |\n",
    "| Test | 73% | 75% | 129 |\n",
    "| Train 2 | 75.1% | 78% | 1291 | \n",
    "| Test 2 | 76.2% | 76.5% | 1291 |\n",
    "| Train 3 | 75.8% | 75.7% | 451 |\n",
    "| Test 3 | 74.1% | 70.1% | 451 |\n",
    "\n"
   ]
  },
  {
   "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": 2
}