Machine Learning - Titanic Disaster.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Simple Tutorial For Beginners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('titanic/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', nan], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Embarked.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1450a4dd488>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn\n",
    "seaborn.set()\n",
    "\n",
    "#--------Survived or Died By Class -----------------#\n",
    "survived_class = dataset[dataset['Survived']==1]['Pclass'].value_counts()\n",
    "dead_class = dataset[dataset['Survived']==0]['Pclass'].value_counts()\n",
    "df_class = pd.DataFrame([survived_class,dead_class])\n",
    "df_class.index = ['Survived','Died']\n",
    "df_class.plot(kind='bar',stacked=True, figsize=(5,3), title=\"Survived/Died by Class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Class 1 that survived: 63.0 %\n",
      "Percentage of Class 2 that survived: 47.0 %\n",
      "Percentage of Class 3 that survived: 24.0 %\n"
     ]
    },
    {
     "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Died</th>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1   2    3\n",
       "Survived  136  87  119\n",
       "Died       80  97  372"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Class1_survived= df_class.iloc[0,0]/df_class.iloc[:,0].sum()*100\n",
    "Class2_survived = df_class.iloc[0,1]/df_class.iloc[:,1].sum()*100\n",
    "Class3_survived = df_class.iloc[0,2]/df_class.iloc[:,2].sum()*100\n",
    "\n",
    "print(\"Percentage of Class 1 that survived:\" ,round(Class1_survived),\"%\")\n",
    "print(\"Percentage of Class 2 that survived:\" ,round(Class2_survived), \"%\")\n",
    "print(\"Percentage of Class 3 that survived:\" ,round(Class3_survived), \"%\")\n",
    "\n",
    "# display table\n",
    "from IPython.display import display\n",
    "display(df_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of female that survived: 74.0 %\n",
      "Percentage of male that survived: 19.0 %\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>233</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Died</th>\n",
       "      <td>81</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          female  male\n",
       "Survived     233   109\n",
       "Died          81   468"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AbNiwBevXb8L+/Zn46afDUqwKkaJxT7CZ+vbth549XQEAXbp0xfDhIwH895mBb731Dr788nOcP38OBw9moX//B2tMf+jQ9zh+/H+Rk3MIAHDrVhlOnjyBwYOHgIjuHYZgM/36oRB3PjOwsNCI8PDnEBz8FEaNGgMXl+44fvw/NcavrrbgpZcWwtt7IgCgqKgIzs7OrV84EdXAw+FWcOzYz+jTxw3Tp8/EQw89jK+/3geLpeYDUYcNG449e/6FqqoqlJWV4aWXnsfRo0ckqphIuexyT7Cqwvz/X+xs+/nawogRo3DixP9i1qxpEELA03MoTp06WWOcqVNDcOHCeTz33AxUV1dj8mQ9hg4dbpPlE1Hj2eV1gkrD6wTljdcJ1o3XCRIR2QGGIBEpmt2EoMyO2mWDvxeilrGLEFSrnXDzZgn/4X9FCIGbN0ugVjtJXQqR3bKLs8Pdumlx/boJpaVFUpciCQcHh1qv5PyFWu2Ebt20dQ4joobZRQi2a6dGjx69pC5DMlptJ1mcZSNqi+zicJiIqLUwBIlI0RiCRKRoDEEiUjSGIBEpWqND8C9/+QuWLFkCAMjKyoJer4ePjw8SEhKs4+Tn5yMoKAi+vr6IiopCVVWV7SsmIrKhRoXgt99+i+Tk209CLi8vR2RkJNauXYv09HTk5eUhMzMTABAREYGYmBhkZGRACIGkpKS7zZaISHINhmBRURESEhIwb948AEBubi7c3d3h5uYGtVoNvV4Pg8GAgoIClJeXw9PTEwAQFBQEg8HQutUTEbVQgxdLx8TEYNGiRbh06RIAoLCwEFrtf+9Q0Ol0MBqNtdq1Wi2MRmOTC7rbI2+UTKvtJHUJRE1mD9vtXUNwx44d6NWrF0aPHo3du3cDACwWC1QqlXUcIQRUKlW97U1V1/MElY53jMibPfyjS0UO221DzxO8awimp6fDZDIhMDAQxcXFKCsrQ0FBQY33aZhMJuh0Ori6usJkMlnbr1y5Ap1OZ4NVICJqPXcNwY0bN1p/3r17N77//nvExsbCx8cHZ8+eRZ8+fZCWlobg4GD07t0bGo0GOTk5GDZsGFJSUuDl5dXqK0BE1BJNfoCCRqNBXFwcFixYALPZDG9vb/j5+QEA4uPjER0djdLSUnh4eGD27Nk2L5iIyJbs4h0jSsc+QXnjO0bqxneMEBHZAYYgESkaQ5CIFI0hSESKxhAkIkVjCBKRojEEiUjRGIJEpGgMQSJSNIYgESkaQ5CIFK3JD1BQim5dnKB20khdhpVcnllXVWHG9eIKqcsgshmGYD3UThreFF+HvlG7ADAEqe3g4TARKRpDkIgUjSFIRIrGECQiRWMIEpGiMQSJSNEaFYIrV67E5MmTMWXKFOsb6LKysqDX6+Hj44OEhATruPn5+QgKCoKvry+ioqJQVVXVOpUTEdlAgyH4/fff47vvvsOePXuwa9cuJCYm4tixY4iMjMTatWuRnp6OvLw8ZGZmAgAiIiIQExODjIwMCCGQlJTU6itBRNRcDYbgyJEjsXnzZqjValy9ehXV1dUoKSmBu7s73NzcoFarodfrYTAYUFBQgPLycnh6egIAgoKCYDAYWn0liIiaq1F3jDg6OmLVqlXYsGED/Pz8UFhYCK1Wax2u0+lgNBprtWu1WhiNxiYVdLdX45E8yOUWPpI/e9hWGn3b3MKFCzF37lzMmzcPZ86cgUqlsg4TQkClUsFisdTZ3hRyee+wPfzxpCKHd8nKCbeV+slhW2nxe4dPnjyJ/Px8AICzszN8fHxw8OBBmEwm6zgmkwk6nQ6urq412q9cuQKdTteS+omIWlWDIXjhwgVER0ejoqICFRUV2Lt3L0JDQ3H69GmcPXsW1dXVSEtLg5eXF3r37g2NRoOcnBwAQEpKCry8vFp9JYiImqvBw2Fvb2/k5uZi6tSpaNeuHXx8fDBlyhS4uLhgwYIFMJvN8Pb2hp+fHwAgPj4e0dHRKC0thYeHB2bPnt3qK0FE1FwqIYT0HXB3kFOfIB+lVVvfqF2y6OeRE24rdZPLttLiPkEioraMIUhEisYQJCJFYwgSkaIxBIlI0RiCRKRoDEEiUjSGIBEpGkOQiBSNIUhEisYQJCJFYwgSkaIxBIlI0Rr9ZGmlsVRWoG/ULqnLkB1LZYXUJRDZFEOwHg6OTtC/liJ1GbKT+l4gALPUZRDZDA+HiUjRGIJEpGgMQSJSNIYgESkaQ5CIFK1RIbhmzRpMmTIFU6ZMwYoVKwAAWVlZ0Ov18PHxQUJCgnXc/Px8BAUFwdfXF1FRUaiqqmqdyomIbKDBEMzKysI333yD5ORk/Otf/8LRo0eRlpaGyMhIrF27Funp6cjLy0NmZiYAICIiAjExMcjIyIAQAklJSa2+EkREzdVgCGq1WixZsgROTk5wdHREv379cObMGbi7u8PNzQ1qtRp6vR4GgwEFBQUoLy+Hp6cnACAoKAgGg6HVV4KIqLkavFi6f//+1p/PnDmDzz77DLNmzYJWq7W263Q6GI1GFBYW1mjXarUwGo1NKuhu7wcledBqO0ldAtkJe9hWGn3HyPHjxxEeHo433ngD7dq1w5kzZ6zDhBBQqVSwWCxQqVS12ptCTi9fp7rJ4YXacsJtpX5y2FZs8vL1nJwcPPvss3jttdfw5JNPwtXVFSaTyTrcZDJBp9PVar9y5Qp0Ol0Lyicial0NhuClS5cwf/58xMfHY8qUKQCAwYMH4/Tp0zh79iyqq6uRlpYGLy8v9O7dGxqNBjk5OQCAlJQUeHl5te4aEBG1QIOHwx999BHMZjPi4uKsbaGhoYiLi8OCBQtgNpvh7e0NPz8/AEB8fDyio6NRWloKDw8PzJ49u/WqJyJqoQZDMDo6GtHR0XUO27NnT622gQMHYufOnS2vjIjoHuAdI0SkaAxBIlI0hiARKRpDkIgUjSFIRIrGECQiRWMIEpGiMQSJSNEYgkSkaAxBIlI0hiARKRpDkIgUjSFIRIrGECQiRWMIEpGiMQSJSNEYgkSkaAxBIlI0hiARKVqjQrC0tBT+/v64cOECACArKwt6vR4+Pj5ISEiwjpefn4+goCD4+voiKioKVVVVrVM1EZGNNBiCP/30E55++mnry9bLy8sRGRmJtWvXIj09HXl5ecjMzAQAREREICYmBhkZGRBCICkpqVWLJyJqqQZDMCkpCcuWLbO+RD03Nxfu7u5wc3ODWq2GXq+HwWBAQUEBysvL4enpCQAICgqCwWBo3eqJiFqowVduvvvuuzU+FxYWQqvVWj/rdDoYjcZa7VqtFkajsckFde/escnT0L2l1XaSugSyE/awrTQYgr9msVigUqmsn4UQUKlU9bY31dWrpbBYRJOnszV7+ONJxWS6IXUJssJtpX5y2FYcHFR33blq8tlhV1dXmEwm62eTyQSdTler/cqVK9ZDaCIiuWpyCA4ePBinT5/G2bNnUV1djbS0NHh5eaF3797QaDTIyckBAKSkpMDLy8vmBRMR2VKTD4c1Gg3i4uKwYMECmM1meHt7w8/PDwAQHx+P6OholJaWwsPDA7Nnz7Z5wUREtqQSQkjfAXcHOfUJ6l9LkboM2Ul9L1AW/Txy0r2rBg6OTlKXITuWygpcLTJLXUaDfYJN3hMkopocHJ34hVmH1PcCAUgfgg3hbXNEpGgMQSJSNIYgESkaQ5CIFI0hSESKxhAkIkVjCBKRojEEiUjRGIJEpGgMQSJSNIYgESkaQ5CIFI0hSESKxhAkIkVjCBKRojEEiUjRGIJEpGgMQSJStFYJwdTUVEyePBk+Pj7YsmVLayyCiMgmbP6OEaPRiISEBOzevRtOTk4IDQ3FI488ggceeMDWiyIiajGbh2BWVhZGjRqFrl27AgB8fX1hMBjw8ssvN2p6BweVrUtqNl03Z6lLkCU5/Y3kgttK3eSwrTRUg81DsLCwEFqt1vpZp9MhNze30dN369bB1iU120fRPlKXIEt3e32hUnFbqZs9bCs27xO0WCxQqf6bvEKIGp+JiOTE5iHo6uoKk8lk/WwymaDT6Wy9GCIim7B5CI4ZMwbffvstrl27hlu3buHzzz+Hl5eXrRdDRGQTNu8T7NmzJxYtWoTZs2ejsrISISEhGDRokK0XQ0RkEyohhJC6CCIiqfCOESJSNIYgESkaQ5CIFI0hSESKxhAkIkVjCBKRojEEiUjRbH6xNDVfdnb2XYePGDHiHlVCpBy8WFpGwsLCAABFRUU4d+4chg4dCgcHBxw+fBgDBgzAJ598InGFJCcDBw6s8XAStVqNdu3awWw2o2PHjg1+qdJt3BOUkcTERADA3LlzsWbNGri7uwMACgoKEBMTI2VpJEPHjh0DACxbtgxDhw5FQEAAVCoVMjIysH//fomrsx/sE5ShixcvWgMQAO677z5cvHhRwopIznJzcxEYGGjdK/T19UVeXp7EVdkP7gnKkIeHB95880088cQTEEIgNTUVw4cPl7oskilnZ2fs2rULTzzxBCwWC1JSUtClSxepy7Ib7BOUoYqKCnz88cf4/vvvAdx+PNmMGTOgVvM7i2orKCjA8uXLcfDgQTg4OGDMmDGIjo5Gz549pS7NLjAEZerChQs4ceIExo0bh0uXLsHNzU3qkkjmioqKrO/2ocZjn6AMpaen449//CPeffddFBcXIzQ0FCkpKVKXRTKVn58PPz8/TJ06FUajEZMmTcLRo0elLstuMARlaP369di2bRs6dOiA7t27Izk5GR9++KHUZZFMvfPOO/jggw/QtWtX9OzZE2+99RaWLVsmdVl2gyEoQw4ODujY8b9v6dLpdHBw4J+K6nbr1i3069fP+nns2LGoqKiQsCL7wp52Gerfvz8+/vhjVFVVIT8/H1u3bsXAgQOlLotkqmvXrjh27Jj1Epk9e/bw7HAT8MSIDJWVlWHdunXIysqCxWLBqFGjMH/+/Bp7h0S/OHfuHN58800cOXIE7du3h7u7O/7617+ib9++UpdmFxiCMrRp0yb4+/uje/fuUpdCdqSsrAwWi4Vflk3Ew2EZunz5MqZNm4a+ffsiICAAkyZNgrOzs9RlkcwsXboUy5cvR1hYWI17iH+xefNmCaqyP9wTlLFDhw4hPT0dBw4cwODBg7FixQqpSyIZycvLw+9//3skJiZCq9VCo9Hg2rVr1mtKR44cKXGF9oF7gjIlhEBlZSUqKyuhUqng6OgodUkkM7169cLMmTNx/Phx/O53vwMAnD59Gp6envjb3/4mbXF2hHuCMvTOO+/giy++wEMPPYSAgAA89thj0Gg0UpdFMhMZGYkePXpgwYIF1i/JiooKrF69GiaTCXFxcRJXaB+4JyhD7u7uSE5OhouLi9SlkIwdPnwYn332WY02JycnLF68GIGBgRJVZX8YgjKyfft2TJ8+HcXFxdi6dWut4S+//LIEVZFc1Xd0oFKpeHF9E/A3JSPsmaCmqOuMcGOGUU3cE5SR0NBQAECnTp14nSA16Pjx43jsscdqtQshYDKZJKjIPjEEZYjXCVJjZGRkSF1Cm8CzwzLG6wSJWh/7BGWK1wkS3RvcE5QhXidIdO+wT1CGXFxceJ0g0T3Cw2EZSk1NZQAS3SPcE5ShBx54AGvWrMHgwYPRvn17a/uIESMkrIqobWKfoAyFhYXValOpVHw0ElErYAgSkaLxcFiG+JBMonuHIShDCxYssP5cVVWFvXv3onPnzhJWRNR28XDYTkybNg07duyQugyiNod7gjJ08eJF689CCJw4cQJFRUUSVkTUdjEEZWjWrFnWPkGVSoVu3bohOjpa4qqI2iYeDsvMvn370K9fP/z2t7/FF198gZ07d+Lhhx/GSy+9xPuHiVoB7xiRkY8++ghr1qxBRUUFjh07hoiICDz++OMoLi7mE2SIWgkPh2UkJSUF27dvh7OzM+Lj4zFx4kRMmzYNQghMnjxZ6vKI2iTuCcqISqWyPjz14MGDePTRR63tRNQ6uCcoI+3atUNJSQnKysqQn5+PsWPHAgAKCgqgVvNPRdQa+J8lIy+++CKmTp2KqqoqhISEQKfTIT09HQkJCZg/f77U5RG1STw7LDNGoxHXr1/HwIEDAQCZmZlo3749HnnkEYkrI2qbGIJEpGg8MUJEisYQJCJFYwgSkaIxBIlI0f4PaHlb5AYAAAADSURBVG2Qnt8zIogAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-------------------Survived/Died by SEX------------------------------------\n",
    "   \n",
    "Survived = dataset[dataset.Survived == 1]['Sex'].value_counts()\n",
    "Died = dataset[dataset.Survived == 0]['Sex'].value_counts()\n",
    "df_sex = pd.DataFrame([Survived , Died])\n",
    "df_sex.index = ['Survived','Died']\n",
    "df_sex.plot(kind='bar',stacked=True, figsize=(5,3), title=\"Survived/Died by Sex\")\n",
    "\n",
    "\n",
    "female_survived= df_sex.female[0]/df_sex.female.sum()*100\n",
    "male_survived = df_sex.male[0]/df_sex.male.sum()*100\n",
    "print(\"Percentage of female that survived:\" ,round(female_survived), \"%\")\n",
    "print(\"Percentage of male that survived:\" ,round(male_survived), \"%\")\n",
    "\n",
    "# display table\n",
    "from IPython.display import display\n",
    "display(df_sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Embark S that survived: 34.0 %\n",
      "Percentage of Embark C that survived: 55.0 %\n",
      "Percentage of Embark Q that survived: 39.0 %\n"
     ]
    },
    {
     "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>S</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>217</td>\n",
       "      <td>93</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Died</th>\n",
       "      <td>427</td>\n",
       "      <td>75</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            S   C   Q\n",
       "Survived  217  93  30\n",
       "Died      427  75  47"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#------Survived / Died By Embarked-------------------#\n",
    "\n",
    "survived_embark = dataset[dataset['Survived'] == 1]['Embarked'].value_counts()\n",
    "dead_embark = dataset[dataset['Survived']==0]['Embarked'].value_counts()\n",
    "df_embark = pd.DataFrame([survived_embark,dead_embark])\n",
    "df_embark.index = ['Survived','Died']\n",
    "df_embark.plot(kind='bar',stacked=True, figsize=(5,3))\n",
    "\n",
    "Embark_S= df_embark.iloc[0,0]/df_embark.iloc[:,0].sum()*100\n",
    "Embark_C = df_embark.iloc[0,1]/df_embark.iloc[:,1].sum()*100\n",
    "Embark_Q = df_embark.iloc[0,2]/df_embark.iloc[:,2].sum()*100\n",
    "print(\"Percentage of Embark S that survived:\", round(Embark_S), \"%\")\n",
    "print(\"Percentage of Embark C that survived:\" ,round(Embark_C), \"%\")\n",
    "print(\"Percentage of Embark Q that survived:\" ,round(Embark_Q), \"%\")\n",
    "\n",
    "from IPython.display import display\n",
    "display(df_embark)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Data cleaning, Feature selection and Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pclass                                               Name     Sex   Age  \\\n",
       "0        3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1        1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2        3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3        1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4        3                           Allen, Mr. William Henry    male  35.0   \n",
       "5        3                                   Moran, Mr. James    male   NaN   \n",
       "6        1                            McCarthy, Mr. Timothy J    male  54.0   \n",
       "7        3                     Palsson, Master. Gosta Leonard    male   2.0   \n",
       "8        3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0   \n",
       "9        2                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0   \n",
       "10       3                    Sandstrom, Miss. Marguerite Rut  female   4.0   \n",
       "11       1                           Bonnell, Miss. Elizabeth  female  58.0   \n",
       "12       3                     Saundercock, Mr. William Henry    male  20.0   \n",
       "13       3                        Andersson, Mr. Anders Johan    male  39.0   \n",
       "14       3               Vestrom, Miss. Hulda Amanda Adolfina  female  14.0   \n",
       "15       2                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0   \n",
       "16       3                               Rice, Master. Eugene    male   2.0   \n",
       "17       2                       Williams, Mr. Charles Eugene    male   NaN   \n",
       "18       3  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0   \n",
       "19       3                            Masselmani, Mrs. Fatima  female   NaN   \n",
       "\n",
       "   Embarked  \n",
       "0         S  \n",
       "1         C  \n",
       "2         S  \n",
       "3         S  \n",
       "4         S  \n",
       "5         Q  \n",
       "6         S  \n",
       "7         S  \n",
       "8         S  \n",
       "9         C  \n",
       "10        S  \n",
       "11        S  \n",
       "12        S  \n",
       "13        S  \n",
       "14        S  \n",
       "15        S  \n",
       "16        Q  \n",
       "17        S  \n",
       "18        S  \n",
       "19        C  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.drop(['PassengerId','Cabin','Ticket','Fare', 'Parch', 'SibSp'], axis=1)\n",
    "y = X.Survived                       # vector of labels (dependent variable)\n",
    "X=X.drop(['Survived'], axis=1)       # remove the dependent variable from the dataframe X\n",
    "\n",
    "X.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.Embarked.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null values in Embarked: 2\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                               Name  Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    1  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina    0  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0   \n",
       "4       3                           Allen, Mr. William Henry    1  35.0   \n",
       "\n",
       "   Embarked_C  Embarked_Q  \n",
       "0           0           0  \n",
       "1           1           0  \n",
       "2           0           0  \n",
       "3           0           0  \n",
       "4           0           0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ----------------- Encoding categorical data -------------------------\n",
    "\n",
    "# encode \"Sex\"\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelEncoder_X = LabelEncoder()\n",
    "X.Sex=labelEncoder_X.fit_transform(X.Sex)\n",
    "\n",
    "\n",
    "# encode \"Embarked\"\n",
    "\n",
    "# number of null values in embarked:\n",
    "print ('Number of null values in Embarked:', sum(X.Embarked.isnull()))\n",
    "\n",
    "# fill the two values with one of the options (S, C or Q)\n",
    "row_index = X.Embarked.isnull()\n",
    "X.loc[row_index,'Embarked']='S' \n",
    "\n",
    "Embarked  = pd.get_dummies(  X.Embarked , prefix='Embarked'  )\n",
    "X = X.drop(['Embarked'], axis=1)\n",
    "X= pd.concat([X, Embarked], axis=1)  \n",
    "# we should drop one of the columns\n",
    "X = X.drop(['Embarked_S'], axis=1)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.Age.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null values in Age: 177\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-------------- Taking care of missing data  -----------------------------\n",
    "\n",
    "print ('Number of null values in Age:', sum(X.Age.isnull()))\n",
    " \n",
    "\n",
    "# -------- Change Name -> Title ----------------------------\n",
    "got= dataset.Name.str.split(',').str[1]\n",
    "X.iloc[:,1]=pd.DataFrame(got).Name.str.split('\\s+').str[1]\n",
    "# ---------------------------------------------------------- \n",
    "\n",
    "\n",
    "#------------------ Average Age per title -------------------------------------------------------------\n",
    "ax = plt.subplot()\n",
    "ax.set_ylabel('Average age')\n",
    "X.groupby('Name').mean()['Age'].plot(kind='bar',figsize=(13,8), ax = ax)\n",
    "\n",
    "title_mean_age=[]\n",
    "title_mean_age.append(list(set(X.Name)))  #set for unique values of the title, and transform into list\n",
    "title_mean_age.append(X.groupby('Name').Age.mean())\n",
    "title_mean_age\n",
    "#------------------------------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "#------------------ Fill the missing Ages ---------------------------\n",
    "n_traning= dataset.shape[0]   #number of rows\n",
    "n_titles= len(title_mean_age[1])\n",
    "for i in range(0, n_traning):\n",
    "    if np.isnan(X.Age[i])==True:\n",
    "        for j in range(0, n_titles):\n",
    "            if X.Name[i] == title_mean_age[0][j]:\n",
    "                X.Age[i] = title_mean_age[1][j]\n",
    "#--------------------------------------------------------------------    \n",
    "\n",
    "X=X.drop(['Name'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "   Pclass  Sex   Age  Embarked_C  Embarked_Q\n",
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    "X.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 70,
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   "source": [
    "for i in range(0, n_traning):\n",
    "    if X.Age[i] > 18:\n",
    "        X.Age[i]= 0\n",
    "    else:\n",
    "        X.Age[i]= 1\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:\n",
      " Accuracy: 0.7721972534332086 +/- 0.024301575403089803 \n",
      "\n",
      "K-NN:\n",
      " Accuracy: 0.8114232209737828 +/- 0.029775280898876405 \n",
      "\n",
      "SVM:\n",
      " Accuracy: 0.793458177278402 +/- 0.026891698745218424 \n",
      "\n",
      "Naive Bayes:\n",
      " Accuracy: 0.7744694132334582 +/- 0.023634858094231933 \n",
      "\n",
      "Random Forest:\n",
      " Accuracy: 0.8136704119850189 +/- 0.03196986282328952\n"
     ]
    }
   ],
   "source": [
    "#-----------------------Logistic Regression---------------------------------------------\n",
    "# Fitting Logistic Regression to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(penalty='l2',random_state = 0)\n",
    "\n",
    "# Applying k-Fold Cross Validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X=X , y=y , cv = 10)\n",
    "print(\"Logistic Regression:\\n Accuracy:\", accuracies.mean(), \"+/-\", accuracies.std(),\"\\n\")\n",
    "\n",
    "\n",
    "\n",
    "#-----------------------------------K-NN --------------------------------------------------\n",
    "\n",
    "# Fitting K-NN to the Training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors = 9, metric = 'minkowski', p = 2)\n",
    "\n",
    "\n",
    "# Applying k-Fold Cross Validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X=X , y=y , cv = 10)\n",
    "print(\"K-NN:\\n Accuracy:\", accuracies.mean(), \"+/-\", accuracies.std(),\"\\n\")\n",
    "\n",
    "\n",
    "#---------------------------------------SVM -------------------------------------------------\n",
    "\n",
    "# Fitting Kernel SVM to the Training set\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
    "\n",
    "# Applying k-Fold Cross Validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X=X , y=y , cv = 10)\n",
    "print(\"SVM:\\n Accuracy:\", accuracies.mean(), \"+/-\", accuracies.std(),\"\\n\")\n",
    "\n",
    "\n",
    "#---------------------------------Naive Bayes-------------------------------------------\n",
    "\n",
    "# Fitting Naive Bayes to the Training set\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier = GaussianNB()\n",
    "\n",
    "# Applying k-Fold Cross Validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X=X , y=y , cv = 10)\n",
    "print(\"Naive Bayes:\\n Accuracy:\", accuracies.mean(), \"+/-\", accuracies.std(),\"\\n\")\n",
    "\n",
    "\n",
    "\n",
    "#----------------------------Random Forest------------------------------------------\n",
    "\n",
    "# Fitting Random Forest Classification to the Training set\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators = 100, criterion = 'entropy', random_state = 0)\n",
    "\n",
    "# Applying k-Fold Cross Validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X=X , y=y , cv = 10)\n",
    "print(\"Random Forest:\\n Accuracy:\", accuracies.mean(), \"+/-\", accuracies.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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