Titanic Kaggle Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    ".output_png {\n",
    "    display: table-cell;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "</style>\n",
    "\"\"\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    "\n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import pylab as plot\n",
    "params = { \n",
    "    'axes.labelsize': \"large\",\n",
    "    'xtick.labelsize': 'x-large',\n",
    "    'legend.fontsize': 20,\n",
    "    'figure.dpi': 150,\n",
    "    'figure.figsize': [25, 7]\n",
    "}\n",
    "plot.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('./titanic/train.csv')\n",
    "print(data.shape)\n",
    "#(891, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId                                               151974\n",
       "Survived                                                     342\n",
       "Pclass                                                       667\n",
       "Name           Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "Sex            femalefemalefemalefemalefemalefemalefemalefema...\n",
       "Age                                                      8219.67\n",
       "SibSp                                                        162\n",
       "Parch                                                        159\n",
       "Ticket         PC 17599STON/O2. 3101282113803347742237736PP 9...\n",
       "Fare                                                     16551.2\n",
       "dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Survived'] == 1].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age'] = data['Age'].fillna(data['Age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Died'] = 1 - data['Survived']\n",
    "data.groupby('Sex').agg('sum')[['Survived', 'Died']].plot(kind='bar', figsize=(25, 7),\n",
    "                                                          stacked=True, color=['g', 'r']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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,
      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('Sex').agg('mean')[['Survived', 'Died']].plot(kind='bar', figsize=(25, 7), \n",
    "                                                           stacked=True, color=['g', 'r']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(25, 7))\n",
    "sns.violinplot(x='Sex', y='Age', \n",
    "               hue='Survived', data=data, \n",
    "               split=True,\n",
    "               palette={0: \"r\", 1: \"g\"}\n",
    "              );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(25, 7))\n",
    "plt.hist([data[data['Survived'] == 1]['Fare'], data[data['Survived'] == 0]['Fare']], \n",
    "         stacked=True, color = ['g','r'],\n",
    "         bins = 50, label = ['Survived','Dead'])\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Number of passengers')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 7))\n",
    "ax = plt.subplot()\n",
    "\n",
    "ax.scatter(data[data['Survived'] == 1]['Age'], data[data['Survived'] == 1]['Fare'], \n",
    "           c='green', s=data[data['Survived'] == 1]['Fare'])\n",
    "ax.scatter(data[data['Survived'] == 0]['Age'], data[data['Survived'] == 0]['Fare'], \n",
    "           c='red', s=data[data['Survived'] == 0]['Fare']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "ax.set_ylabel('Average fare')\n",
    "data.groupby('Pclass').mean()['Fare'].plot(kind='bar', figsize=(25, 7), ax = ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(25, 7))\n",
    "sns.violinplot(x='Embarked', y='Fare', hue='Survived', data=data, split=True, palette={0: \"r\", 1: \"g\"});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def status(feature):\n",
    "    print('Processing', feature, ': ok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_combined_data():\n",
    "    # reading train data\n",
    "    train = pd.read_csv('./titanic/train.csv')\n",
    "    \n",
    "    # reading test data\n",
    "    test = pd.read_csv('./titanic/test.csv')\n",
    "\n",
    "    # extracting and then removing the targets from the training data \n",
    "    targets = train.Survived\n",
    "    train.drop(['Survived'], 1, inplace=True)\n",
    "    \n",
    "\n",
    "    # merging train data and test data for future feature engineering\n",
    "    # we'll also remove the PassengerID since this is not an informative feature\n",
    "    combined = train.append(test)\n",
    "    combined.reset_index(inplace=True)\n",
    "    combined.drop(['index', 'PassengerId'], inplace=True, axis=1)\n",
    "    \n",
    "    return combined\n",
    "\n",
    "combined = get_combined_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1309, 10)\n"
     ]
    }
   ],
   "source": [
    "print(combined.shape)\n",
    "# (1309, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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>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>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>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>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>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1      1      0          PC 17599  71.2833   C85        C  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      1      0            113803  53.1000  C123        S  \n",
       "4      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Different Name titles in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Major', 'Jonkheer', 'the Countess', 'Rev', 'Mlle', 'Mrs', 'Sir', 'Ms', 'Dr', 'Master', 'Don', 'Col', 'Mr', 'Lady', 'Capt', 'Mme', 'Miss'}\n"
     ]
    }
   ],
   "source": [
    "titles = set()\n",
    "for name in data['Name']:\n",
    "    titles.add(name.split(',')[1].split('.')[0].strip())\n",
    "\n",
    "print(titles)\n",
    "# set(['Sir', 'Major', 'the Countess', 'Don', 'Mlle', 'Capt', 'Dr', 'Lady', 'Rev', 'Mrs', 'Jonkheer', 'Master', 'Ms', 'Mr', 'Mme', 'Miss', 'Col'])\n",
    "\n",
    "Title_Dictionary = {\n",
    "    \"Capt\": \"Officer\",\n",
    "    \"Col\": \"Officer\",\n",
    "    \"Major\": \"Officer\",\n",
    "    \"Jonkheer\": \"Royalty\",\n",
    "    \"Don\": \"Royalty\",\n",
    "    \"Sir\" : \"Royalty\",\n",
    "    \"Dr\": \"Officer\",\n",
    "    \"Rev\": \"Officer\",\n",
    "    \"the Countess\":\"Royalty\",\n",
    "    \"Mme\": \"Mrs\",\n",
    "    \"Mlle\": \"Miss\",\n",
    "    \"Ms\": \"Mrs\",\n",
    "    \"Mr\" : \"Mr\",\n",
    "    \"Mrs\" : \"Mrs\",\n",
    "    \"Miss\" : \"Miss\",\n",
    "    \"Master\" : \"Master\",\n",
    "    \"Lady\" : \"Royalty\"\n",
    "}\n",
    "\n",
    "def get_titles():\n",
    "    # we extract the title from each name\n",
    "    combined['Title'] = combined['Name'].map(lambda name:name.split(',')[1].split('.')[0].strip())\n",
    "    \n",
    "    # a map of more aggregated title\n",
    "    # we map each title\n",
    "    combined['Title'] = combined.Title.map(Title_Dictionary)\n",
    "    status('Title')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Title : ok\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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "    </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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "    </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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "    </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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "    </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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "    </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",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked Title  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       "1      1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       "3      1      0            113803  53.1000  C123        S   Mrs  \n",
       "4      0      0            373450   8.0500   NaN        S    Mr  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = get_titles()\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pclass                          Name     Sex   Age  SibSp  Parch  \\\n",
       "1305       1  Oliva y Ocana, Dona. Fermina  female  39.0      0      0   \n",
       "\n",
       "        Ticket   Fare Cabin Embarked Title  \n",
       "1305  PC 17758  108.9  C105        C   NaN  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[combined['Title'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "177\n"
     ]
    }
   ],
   "source": [
    "print(combined.iloc[:891].Age.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\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>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Officer</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Royalty</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>Miss</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Pclass    Title   Age\n",
       "0  female       1     Miss  30.0\n",
       "1  female       1      Mrs  40.0\n",
       "2  female       1  Officer  49.0\n",
       "3  female       1  Royalty  40.5\n",
       "4  female       2     Miss  24.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(combined.iloc[891:].Age.isnull().sum())\n",
    "# 86\n",
    "\n",
    "grouped_train = combined.iloc[:891].groupby(['Sex','Pclass','Title'])\n",
    "grouped_median_train = grouped_train.median()\n",
    "grouped_median_train = grouped_median_train.reset_index()[['Sex', 'Pclass', 'Title', 'Age']]\n",
    "\n",
    "grouped_median_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing age : ok\n"
     ]
    }
   ],
   "source": [
    "def fill_age(row):\n",
    "    condition = (\n",
    "        (grouped_median_train['Sex'] == row['Sex']) & \n",
    "        (grouped_median_train['Title'] == row['Title']) & \n",
    "        (grouped_median_train['Pclass'] == row['Pclass'])\n",
    "    ) \n",
    "    return grouped_median_train[condition]['Age'].values[0]\n",
    "\n",
    "\n",
    "def process_age():\n",
    "    global combined\n",
    "    # a function that fills the missing values of the Age variable\n",
    "    combined['Age'] = combined.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)\n",
    "    status('age')\n",
    "    return combined\n",
    "\n",
    "combined = process_age()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_names():\n",
    "    global combined\n",
    "    # we clean the Name variable\n",
    "    combined.drop('Name', axis=1, inplace=True)\n",
    "    \n",
    "    # encoding in dummy variable\n",
    "    titles_dummies = pd.get_dummies(combined['Title'], prefix='Title')\n",
    "    combined = pd.concat([combined, titles_dummies], axis=1)\n",
    "    \n",
    "    # removing the title variable\n",
    "    combined.drop('Title', axis=1, inplace=True)\n",
    "    \n",
    "    status('names')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing names : ok\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
       "0       3    male  22.0      1      0         A/5 21171   7.2500   NaN   \n",
       "1       1  female  38.0      1      0          PC 17599  71.2833   C85   \n",
       "2       3  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
       "3       1  female  35.0      1      0            113803  53.1000  C123   \n",
       "4       3    male  35.0      0      0            373450   8.0500   NaN   \n",
       "\n",
       "  Embarked  Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Officer  \\\n",
       "0        S             0           0         1          0              0   \n",
       "1        C             0           0         0          1              0   \n",
       "2        S             0           1         0          0              0   \n",
       "3        S             0           0         0          1              0   \n",
       "4        S             0           0         1          0              0   \n",
       "\n",
       "   Title_Royalty  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = process_names()\n",
    "\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_fares():\n",
    "    global combined\n",
    "    # there's one missing fare value - replacing it with the mean.\n",
    "    combined.Fare.fillna(combined.iloc[:891].Fare.mean(), inplace=True)\n",
    "    status('fare')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing fare : ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_fares()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_embarked():\n",
    "    global combined\n",
    "    # two missing embarked values - filling them with the most frequent one in the train  set(S)\n",
    "    combined.Embarked.fillna('S', inplace=True)\n",
    "    # dummy encoding \n",
    "    embarked_dummies = pd.get_dummies(combined['Embarked'], prefix='Embarked')\n",
    "    combined = pd.concat([combined, embarked_dummies], axis=1)\n",
    "    combined.drop('Embarked', axis=1, inplace=True)\n",
    "    status('embarked')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing embarked : ok\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>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>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</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>3</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>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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</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>0</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>4</th>\n",
       "      <td>3</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>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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
       "0       3    male  22.0      1      0         A/5 21171   7.2500   NaN   \n",
       "1       1  female  38.0      1      0          PC 17599  71.2833   C85   \n",
       "2       3  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
       "3       1  female  35.0      1      0            113803  53.1000  C123   \n",
       "4       3    male  35.0      0      0            373450   8.0500   NaN   \n",
       "\n",
       "   Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Officer  \\\n",
       "0             0           0         1          0              0   \n",
       "1             0           0         0          1              0   \n",
       "2             0           1         0          0              0   \n",
       "3             0           0         0          1              0   \n",
       "4             0           0         1          0              0   \n",
       "\n",
       "   Title_Royalty  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0              0           0           0           1  \n",
       "1              0           1           0           0  \n",
       "2              0           0           0           1  \n",
       "3              0           0           0           1  \n",
       "4              0           0           0           1  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = process_embarked()\n",
    "\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'B', 'A', 'T', 'G', 'F', 'U', 'D', 'E', 'C'}\n",
      "{'B', 'A', 'G', 'F', 'U', 'D', 'E', 'C'}\n"
     ]
    }
   ],
   "source": [
    "train_cabin, test_cabin = set(), set()\n",
    "\n",
    "for c in combined.iloc[:891]['Cabin']:\n",
    "    try:\n",
    "        train_cabin.add(c[0])\n",
    "    except:\n",
    "        train_cabin.add('U')\n",
    "        \n",
    "for c in combined.iloc[891:]['Cabin']:\n",
    "    try:\n",
    "        test_cabin.add(c[0])\n",
    "    except:\n",
    "        test_cabin.add('U')\n",
    "\n",
    "print(train_cabin)\n",
    "# set(['A', 'C', 'B', 'E', 'D', 'G', 'F', 'U', 'T'])\n",
    "\n",
    "print(test_cabin)\n",
    "# set(['A', 'C', 'B', 'E', 'D', 'G', 'F', 'U'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_cabin():\n",
    "    global combined    \n",
    "    # replacing missing cabins with U (for Uknown)\n",
    "    combined.Cabin.fillna('U', inplace=True)\n",
    "    \n",
    "    # mapping each Cabin value with the cabin letter\n",
    "    combined['Cabin'] = combined['Cabin'].map(lambda c: c[0])\n",
    "    \n",
    "    # dummy encoding ...\n",
    "    cabin_dummies = pd.get_dummies(combined['Cabin'], prefix='Cabin')    \n",
    "    combined = pd.concat([combined, cabin_dummies], axis=1)\n",
    "\n",
    "    combined.drop('Cabin', axis=1, inplace=True)\n",
    "    status('cabin')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing cabin : ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_cabin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <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>1</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>0</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",
       "      <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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3       1  female  35.0      1      0            113803  53.1000   \n",
       "4       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "   Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Officer  \\\n",
       "0             0           0         1          0              0   \n",
       "1             0           0         0          1              0   \n",
       "2             0           1         0          0              0   \n",
       "3             0           0         0          1              0   \n",
       "4             0           0         1          0              0   \n",
       "\n",
       "   Title_Royalty  Embarked_C  Embarked_Q  Embarked_S  Cabin_A  Cabin_B  \\\n",
       "0              0           0           0           1        0        0   \n",
       "1              0           1           0           0        0        0   \n",
       "2              0           0           0           1        0        0   \n",
       "3              0           0           0           1        0        0   \n",
       "4              0           0           0           1        0        0   \n",
       "\n",
       "   Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_U  \n",
       "0        0        0        0        0        0        0        1  \n",
       "1        1        0        0        0        0        0        0  \n",
       "2        0        0        0        0        0        0        1  \n",
       "3        1        0        0        0        0        0        0  \n",
       "4        0        0        0        0        0        0        1  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_sex():\n",
    "    global combined\n",
    "    # mapping string values to numerical one \n",
    "    combined['Sex'] = combined['Sex'].map({'male':1, 'female':0})\n",
    "    status('Sex')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Sex : ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_sex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_pclass():\n",
    "    \n",
    "    global combined\n",
    "    # encoding into 3 categories:\n",
    "    pclass_dummies = pd.get_dummies(combined['Pclass'], prefix=\"Pclass\")\n",
    "    \n",
    "    # adding dummy variable\n",
    "    combined = pd.concat([combined, pclass_dummies],axis=1)\n",
    "    \n",
    "    # removing \"Pclass\"\n",
    "    combined.drop('Pclass',axis=1,inplace=True)\n",
    "    \n",
    "    status('Pclass')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Pclass : ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_pclass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "object of type 'filter' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-128-042d7a151ac2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     45\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcombined\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m \u001b[0mcombined\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprocess_ticket\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-128-042d7a151ac2>\u001b[0m in \u001b[0;36mprocess_ticket\u001b[1;34m()\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[1;31m# Extracting dummy variables from tickets:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m     \u001b[0mcombined\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Ticket'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcombined\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Ticket'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcleanTicket\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m     \u001b[0mtickets_dummies\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcombined\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Ticket'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Ticket'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m     \u001b[0mcombined\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcombined\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtickets_dummies\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\matovu moses\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, arg, na_action)\u001b[0m\n\u001b[0;32m   3628\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3629\u001b[0m         \"\"\"\n\u001b[1;32m-> 3630\u001b[1;33m         \u001b[0mnew_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_map_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mna_action\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3631\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3632\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\matovu moses\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\base.py\u001b[0m in \u001b[0;36m_map_values\u001b[1;34m(self, mapper, na_action)\u001b[0m\n\u001b[0;32m   1143\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1144\u001b[0m         \u001b[1;31m# mapper is a function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1145\u001b[1;33m         \u001b[0mnew_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1146\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1147\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m<ipython-input-128-042d7a151ac2>\u001b[0m in \u001b[0;36mcleanTicket\u001b[1;34m(ticket)\u001b[0m\n\u001b[0;32m     29\u001b[0m         \u001b[0mticket\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mticket\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m         \u001b[0mticket\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misdigit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mticket\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 31\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticket\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     32\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mticket\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: object of type 'filter' has no len()"
     ]
    }
   ],
   "source": [
    "def cleanTicket(ticket):\n",
    "    ticket = ticket.replace('.', '')\n",
    "    ticket = ticket.replace('/', '')\n",
    "    ticket = ticket.split()\n",
    "    ticket = map(lambda t : t.strip(), ticket)\n",
    "    ticket = list(filter(lambda t : not t.isdigit(), ticket))\n",
    "    if len(ticket) > 0:\n",
    "        return ticket[0]\n",
    "    else: \n",
    "        return 'XXX'\n",
    "\n",
    "tickets = set()\n",
    "for t in combined['Ticket']:\n",
    "    tickets.add(cleanTicket(t))\n",
    "\n",
    "print(len(tickets))\n",
    "#37\n",
    "\n",
    "\n",
    "def process_ticket():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # a function that extracts each prefix of the ticket, returns 'XXX' if no prefix (i.e the ticket is a digit)\n",
    "    def cleanTicket(ticket):\n",
    "        ticket = ticket.replace('.','')\n",
    "        ticket = ticket.replace('/','')\n",
    "        ticket = ticket.split()\n",
    "        ticket = map(lambda t : t.strip(), ticket)\n",
    "        ticket = filter(lambda t : not t.isdigit(), ticket)\n",
    "        if len(ticket) > 0:\n",
    "            return ticket[0]\n",
    "        else: \n",
    "            return 'XXX'\n",
    "    \n",
    "\n",
    "    # Extracting dummy variables from tickets:\n",
    "\n",
    "    combined['Ticket'] = combined['Ticket'].map(cleanTicket)\n",
    "    tickets_dummies = pd.get_dummies(combined['Ticket'], prefix='Ticket')\n",
    "    combined = pd.concat([combined, tickets_dummies], axis=1)\n",
    "    combined.drop('Ticket', inplace=True, axis=1)\n",
    "\n",
    "    status('Ticket')\n",
    "    return combined\n",
    "\n",
    "combined = process_ticket()"
   ]
  }
 ],
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