Titanic Kaggle challenge_80.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('train.csv')\n",
    "data.head()\n",
    "#data.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25987d35940>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25987d25b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising survival based on gender\n",
    "\n",
    "survived_sex = data[data['Survived']==1]['Sex'].value_counts()\n",
    "dead_sex = data[data['Survived']== 0]['Sex'].value_counts()\n",
    "df = pd.DataFrame([survived_sex, dead_sex])\n",
    "df.index = ['Survived', 'Dead']\n",
    "df.plot(kind = 'bar', stacked = True, figsize=(15,8))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files (x86)\\python35\\lib\\site-packages\\numpy\\core\\fromnumeric.py:52: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n",
      "c:\\program files (x86)\\python35\\lib\\site-packages\\numpy\\core\\_methods.py:29: RuntimeWarning: invalid value encountered in reduce\n",
      "  return umr_minimum(a, axis, None, out, keepdims)\n",
      "c:\\program files (x86)\\python35\\lib\\site-packages\\numpy\\core\\_methods.py:26: RuntimeWarning: invalid value encountered in reduce\n",
      "  return umr_maximum(a, axis, None, out, keepdims)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "max must be larger than min in range parameter.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-9c5a1a6bfa3b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# Survival based on Age\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mfigure\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Survived'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Age'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Survived'\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;34m'Age'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'g'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'Survived'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Dead'\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      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Age\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Number of passengers\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python35\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, hold, data, **kwargs)\u001b[0m\n\u001b[0;32m   3023\u001b[0m                       \u001b[0mhisttype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malign\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malign\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3024\u001b[0m                       \u001b[0mrwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3025\u001b[1;33m                       stacked=stacked, normed=normed, data=data, **kwargs)\n\u001b[0m\u001b[0;32m   3026\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3027\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python35\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1715\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m   1716\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1717\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1718\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1719\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python35\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mhist\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m   6159\u001b[0m             \u001b[1;31m# this will automatically overwrite bins,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6160\u001b[0m             \u001b[1;31m# so that each histogram uses the same bins\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6161\u001b[1;33m             \u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mhist_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6162\u001b[0m             \u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# causes problems later if it's an int\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6163\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmlast\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\program files (x86)\\python35\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mhistogram\u001b[1;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[0;32m    665\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfirst_edge\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mlast_edge\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    666\u001b[0m         raise ValueError(\n\u001b[1;32m--> 667\u001b[1;33m             'max must be larger than min in range parameter.')\n\u001b[0m\u001b[0;32m    668\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfirst_edge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlast_edge\u001b[0m\u001b[1;33m]\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[0;32m    669\u001b[0m         raise ValueError(\n",
      "\u001b[1;31mValueError\u001b[0m: max must be larger than min in range parameter."
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25987d25e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure = plt.figure(figsize=(15,8))\n",
    "# plt.hist([data[data['Survived'] == 1]['Age'], data[data['Survived'] == 0]['Age']], stacked = True, color=['g', 'r'], bins = 30, label = ['Survived', 'Dead'])\n",
    "# plt.xlabel('Fare')\n",
    "# plt.ylabel('No of Passengers')\n",
    "# plt.legend()\n",
    "# Survival based on Age\n",
    "figure = plt.figure(figsize=(15,8))\n",
    "plt.hist([data[data['Survived']==1]['Age'], data[data['Survived']==0]['Age']], stacked=True, color = ['g','r'],label = ['Survived','Dead'])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Number of passengers\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files (x86)\\python35\\lib\\site-packages\\numpy\\core\\fromnumeric.py:52: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25987d9def0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25987da86a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survival based on fare\n",
    "\n",
    "figure = plt.figure(figsize=(15,8))\n",
    "plt.hist([data[data['Survived']==1]['Fare'],data[data['Survived']==0]['Fare']], stacked=True, color = ['g','r'],\n",
    "         bins = 70,label = ['Survived','Dead'])\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Number of passengers')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = pd.read_csv('test.csv')\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def status(feature):\n",
    "    print(\"Processing \" + feature + \": OK\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combined_data():\n",
    "    train = pd.read_csv('train.csv')\n",
    "    test = pd.read_csv('test.csv')\n",
    "    \n",
    "    targets = train.Survived\n",
    "    train.drop('Survived', 1, inplace = True)\n",
    "    \n",
    "    combined = train.append(test)\n",
    "    combined.reset_index(inplace = True)\n",
    "    combined.drop('index', inplace = True, axis = 1)\n",
    "    \n",
    "    return combined\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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>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  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = combined_data()\n",
    "\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_titles():\n",
    "    combined['Title'] = combined['Name'].map(lambda name: name.split(',')[1].split('.')[0].strip())\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",
    "                        \"Dona\":       \"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",
    "    combined['Title'] = combined.Title.map(Title_Dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
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       "      <td>Mr</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</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",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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",
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       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked Title  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S   Mrs  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S    Mr  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_titles()\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_train = combined.head(891).groupby(['Sex','Pclass','Title'])\n",
    "grouped_median_train = grouped_train.median()\n",
    "\n",
    "grouped_test = combined.iloc[891:].groupby(['Sex','Pclass','Title'])\n",
    "grouped_median_test = grouped_test.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">female</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Miss</th>\n",
       "      <td>369.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>499.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>797.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.92920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royalty</th>\n",
       "      <td>658.5</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>Miss</th>\n",
       "      <td>437.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>439.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Miss</th>\n",
       "      <td>372.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.75625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>405.5</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.97500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">male</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Master</th>\n",
       "      <td>446.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>463.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>648.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royalty</th>\n",
       "      <td>600.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.72080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>Master</th>\n",
       "      <td>408.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>440.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>358.5</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Master</th>\n",
       "      <td>270.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.51250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>472.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.89580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PassengerId   Age  SibSp  Parch       Fare\n",
       "Sex    Pclass Title                                              \n",
       "female 1      Miss           369.0  30.0    0.0    0.0   88.25000\n",
       "              Mrs            499.0  40.0    1.0    0.0   79.20000\n",
       "              Officer        797.0  49.0    0.0    0.0   25.92920\n",
       "              Royalty        658.5  40.5    0.5    0.0   63.05000\n",
       "       2      Miss           437.5  24.0    0.0    0.0   13.00000\n",
       "              Mrs            439.5  31.5    1.0    0.0   26.00000\n",
       "       3      Miss           372.0  18.0    0.0    0.0    8.75625\n",
       "              Mrs            405.5  31.0    1.0    1.0   15.97500\n",
       "male   1      Master         446.0   4.0    1.0    2.0  120.00000\n",
       "              Mr             463.0  40.0    0.0    0.0   42.40000\n",
       "              Officer        648.0  51.0    0.0    0.0   35.50000\n",
       "              Royalty        600.0  40.0    0.0    0.0   27.72080\n",
       "       2      Master         408.0   1.0    1.0    1.0   26.00000\n",
       "              Mr             440.0  31.0    0.0    0.0   13.00000\n",
       "              Officer        358.5  46.5    0.0    0.0   13.00000\n",
       "       3      Master         270.5   4.0    3.5    1.0   28.51250\n",
       "              Mr             472.0  26.0    0.0    0.0    7.89580"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_median_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">female</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>Miss</th>\n",
       "      <td>1074.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>158.20835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>1076.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.35830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royalty</th>\n",
       "      <td>1306.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>Miss</th>\n",
       "      <td>1121.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>1123.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Miss</th>\n",
       "      <td>1090.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.87920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>1048.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.28125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">male</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>Master</th>\n",
       "      <td>1022.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>198.43750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>1102.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.24790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>1094.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.85830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>Master</th>\n",
       "      <td>1033.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>27.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>1156.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>1048.5</td>\n",
       "      <td>35.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>19.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Master</th>\n",
       "      <td>1173.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.24580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>1101.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.85420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PassengerId   Age  SibSp  Parch       Fare\n",
       "Sex    Pclass Title                                              \n",
       "female 1      Miss          1074.0  32.0    0.0    0.0  158.20835\n",
       "              Mrs           1076.0  48.0    1.0    0.0   63.35830\n",
       "              Royalty       1306.0  39.0    0.0    0.0  108.90000\n",
       "       2      Miss          1121.0  19.5    1.0    1.0   24.50000\n",
       "              Mrs           1123.5  29.0    0.0    0.0   26.00000\n",
       "       3      Miss          1090.5  22.0    0.0    0.0    7.87920\n",
       "              Mrs           1048.0  28.0    1.0    0.5   14.28125\n",
       "male   1      Master        1022.0   9.5    1.0    2.0  198.43750\n",
       "              Mr            1102.0  42.0    0.0    0.0   50.24790\n",
       "              Officer       1094.0  53.0    1.0    0.0   81.85830\n",
       "       2      Master        1033.5   5.0    0.5    1.5   27.75000\n",
       "              Mr            1156.0  28.0    0.0    0.0   13.00000\n",
       "              Officer       1048.5  35.5    0.5    0.5   19.50000\n",
       "       3      Master        1173.0   7.0    1.0    1.0   15.24580\n",
       "              Mr            1101.0  25.0    0.0    0.0    7.85420"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_median_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_age():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # a function that fills the missing values of the Age variable\n",
    "    \n",
    "    def fillAges(row, grouped_median):\n",
    "        if row['Sex']=='female' and row['Pclass'] == 1:\n",
    "            if row['Title'] == 'Miss':\n",
    "                return grouped_median.loc['female', 1, 'Miss']['Age']\n",
    "            elif row['Title'] == 'Mrs':\n",
    "                return grouped_median.loc['female', 1, 'Mrs']['Age']\n",
    "            elif row['Title'] == 'Officer':\n",
    "                return grouped_median.loc['female', 1, 'Officer']['Age']\n",
    "            elif row['Title'] == 'Royalty':\n",
    "                return grouped_median.loc['female', 1, 'Royalty']['Age']\n",
    "\n",
    "        elif row['Sex']=='female' and row['Pclass'] == 2:\n",
    "            if row['Title'] == 'Miss':\n",
    "                return grouped_median.loc['female', 2, 'Miss']['Age']\n",
    "            elif row['Title'] == 'Mrs':\n",
    "                return grouped_median.loc['female', 2, 'Mrs']['Age']\n",
    "\n",
    "        elif row['Sex']=='female' and row['Pclass'] == 3:\n",
    "            if row['Title'] == 'Miss':\n",
    "                return grouped_median.loc['female', 3, 'Miss']['Age']\n",
    "            elif row['Title'] == 'Mrs':\n",
    "                return grouped_median.loc['female', 3, 'Mrs']['Age']\n",
    "\n",
    "        elif row['Sex']=='male' and row['Pclass'] == 1:\n",
    "            if row['Title'] == 'Master':\n",
    "                return grouped_median.loc['male', 1, 'Master']['Age']\n",
    "            elif row['Title'] == 'Mr':\n",
    "                return grouped_median.loc['male', 1, 'Mr']['Age']\n",
    "            elif row['Title'] == 'Officer':\n",
    "                return grouped_median.loc['male', 1, 'Officer']['Age']\n",
    "            elif row['Title'] == 'Royalty':\n",
    "                return grouped_median.loc['male', 1, 'Royalty']['Age']\n",
    "\n",
    "        elif row['Sex']=='male' and row['Pclass'] == 2:\n",
    "            if row['Title'] == 'Master':\n",
    "                return grouped_median.loc['male', 2, 'Master']['Age']\n",
    "            elif row['Title'] == 'Mr':\n",
    "                return grouped_median.loc['male', 2, 'Mr']['Age']\n",
    "            elif row['Title'] == 'Officer':\n",
    "                return grouped_median.loc['male', 2, 'Officer']['Age']\n",
    "\n",
    "        elif row['Sex']=='male' and row['Pclass'] == 3:\n",
    "            if row['Title'] == 'Master':\n",
    "                return grouped_median.loc['male', 3, 'Master']['Age']\n",
    "            elif row['Title'] == 'Mr':\n",
    "                return grouped_median.loc['male', 3, 'Mr']['Age']\n",
    "    \n",
    "    combined.head(891).Age = combined.head(891).apply(lambda r : fillAges(r, grouped_median_train) if np.isnan(r['Age']) \n",
    "                                                      else r['Age'], axis=1)\n",
    "    \n",
    "    combined.iloc[891:].Age = combined.iloc[891:].apply(lambda r : fillAges(r, grouped_median_test) if np.isnan(r['Age']) \n",
    "                                                      else r['Age'], axis=1)\n",
    "    \n",
    "    status('age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing age: OK\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek Singh\\AppData\\Roaming\\Python\\Python35\\site-packages\\pandas\\core\\generic.py:3110: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "process_age()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1309 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Title          1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(6)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "combined.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add dummy titles viz., Title_master, Title_miss etc\n",
    "def process_names():\n",
    "    \n",
    "    global combined\n",
    "    combined.drop('Name',axis=1,inplace=True)\n",
    "    title_dummies = pd.get_dummies(combined['Title'],prefix='Title')\n",
    "    combined = pd.concat([combined,title_dummies],axis=1)\n",
    "    combined.drop('Title',axis=1,inplace=True)\n",
    "    \n",
    "    status('names')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing names: OK\n"
     ]
    }
   ],
   "source": [
    "process_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\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>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>4</td>\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>5</td>\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": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0            1       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1            2       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2            3       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3            4       1  female  35.0      1      0            113803  53.1000   \n",
       "4            5       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Cabin Embarked  Title_Master  Title_Miss  Title_Mr  Title_Mrs  \\\n",
       "0   NaN        S             0           0         1          0   \n",
       "1   C85        C             0           0         0          1   \n",
       "2   NaN        S             0           1         0          0   \n",
       "3  C123        S             0           0         0          1   \n",
       "4   NaN        S             0           0         1          0   \n",
       "\n",
       "   Title_Officer  Title_Royalty  \n",
       "0              0              0  \n",
       "1              0              0  \n",
       "2              0              0  \n",
       "3              0              0  \n",
       "4              0              0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the missing fare value with mean of all fares\n",
    "def process_fares():\n",
    "    \n",
    "    global combined\n",
    "    combined.head(891).Fare.fillna(combined.head(891).Fare.mean(), inplace=True)\n",
    "    combined.iloc[891:].Fare.fillna(combined.iloc[891:].Fare.mean(), inplace=True)\n",
    "    \n",
    "    status('fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing fare: OK\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek Singh\\AppData\\Roaming\\Python\\Python35\\site-packages\\pandas\\core\\generic.py:3660: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "process_fares()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_embarked():\n",
    "    \n",
    "    global combined\n",
    "    # two missing embarked values - filling them with the most frequent one (S)\n",
    "    combined.head(891).Embarked.fillna('S', inplace=True)\n",
    "    combined.iloc[891:].Embarked.fillna('S', inplace=True)\n",
    "    \n",
    "    \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",
    "    \n",
    "    status('embarked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_cabin():\n",
    "    \n",
    "    global combined\n",
    "    \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",
    "    \n",
    "    combined = pd.concat([combined,cabin_dummies], axis=1)\n",
    "    \n",
    "    combined.drop('Cabin', axis=1, inplace=True)\n",
    "    \n",
    "    status('cabin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing cabin: OK\n"
     ]
    }
   ],
   "source": [
    "process_cabin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</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>Embarked</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>...</th>\n",
       "      <th>Title_Royalty</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>1</td>\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>S</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\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>C</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
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       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\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>S</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>4</td>\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>S</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\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>S</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0            1       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1            2       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2            3       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3            4       1  female  35.0      1      0            113803  53.1000   \n",
       "4            5       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Embarked  Title_Master   ...     Title_Royalty  Cabin_A  Cabin_B  Cabin_C  \\\n",
       "0        S             0   ...                 0        0        0        0   \n",
       "1        C             0   ...                 0        0        0        1   \n",
       "2        S             0   ...                 0        0        0        0   \n",
       "3        S             0   ...                 0        0        0        1   \n",
       "4        S             0   ...                 0        0        0        0   \n",
       "\n",
       "   Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_U  \n",
       "0        0        0        0        0        0        1  \n",
       "1        0        0        0        0        0        0  \n",
       "2        0        0        0        0        0        1  \n",
       "3        0        0        0        0        0        0  \n",
       "4        0        0        0        0        0        1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_sex():\n",
    "    \n",
    "    global combined\n",
    "    # mapping string values to numerical one \n",
    "    combined['Sex'] = combined['Sex'].map({'male':1,'female':0})\n",
    "    \n",
    "    status('sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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 variables\n",
    "    combined = pd.concat([combined,pclass_dummies],axis=1)\n",
    "    \n",
    "    # removing \"Pclass\"\n",
    "    \n",
    "    combined.drop('Pclass',axis=1,inplace=True)\n",
    "    \n",
    "    status('pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing pclass: OK\n"
     ]
    }
   ],
   "source": [
    "process_pclass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "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 = list(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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing ticket: OK\n"
     ]
    },
    {
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       "  <tbody>\n",
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       "    </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>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId     Sex   Age  SibSp  Parch     Fare Embarked  Title_Master  \\\n",
       "0            1    male  22.0      1      0   7.2500        S             0   \n",
       "1            2  female  38.0      1      0  71.2833        C             0   \n",
       "2            3  female  26.0      0      0   7.9250        S             0   \n",
       "3            4  female  35.0      1      0  53.1000        S             0   \n",
       "4            5    male  35.0      0      0   8.0500        S             0   \n",
       "\n",
       "   Title_Miss  Title_Mr     ...      Ticket_SOTONO2  Ticket_SOTONOQ  \\\n",
       "0           0         1     ...                   0               0   \n",
       "1           0         0     ...                   0               0   \n",
       "2           1         0     ...                   0               0   \n",
       "3           0         0     ...                   0               0   \n",
       "4           0         1     ...                   0               0   \n",
       "\n",
       "   Ticket_SP  Ticket_STONO  Ticket_STONO2  Ticket_STONOQ  Ticket_SWPP  \\\n",
       "0          0             0              0              0            0   \n",
       "1          0             0              0              0            0   \n",
       "2          0             0              1              0            0   \n",
       "3          0             0              0              0            0   \n",
       "4          0             0              0              0            0   \n",
       "\n",
       "   Ticket_WC  Ticket_WEP  Ticket_XXX  \n",
       "0          0           0           0  \n",
       "1          0           0           0  \n",
       "2          0           0           0  \n",
       "3          0           0           1  \n",
       "4          0           0           1  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "process_ticket()\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>...</th>\n",
       "      <th>Ticket_SOTONO2</th>\n",
       "      <th>Ticket_SOTONOQ</th>\n",
       "      <th>Ticket_SP</th>\n",
       "      <th>Ticket_STONO</th>\n",
       "      <th>Ticket_STONO2</th>\n",
       "      <th>Ticket_STONOQ</th>\n",
       "      <th>Ticket_SWPP</th>\n",
       "      <th>Ticket_WC</th>\n",
       "      <th>Ticket_WEP</th>\n",
       "      <th>Ticket_XXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </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>7.9250</td>\n",
       "      <td>S</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId     Sex   Age  SibSp  Parch     Fare Embarked  Title_Master  \\\n",
       "0            1    male  22.0      1      0   7.2500        S             0   \n",
       "1            2  female  38.0      1      0  71.2833        C             0   \n",
       "2            3  female  26.0      0      0   7.9250        S             0   \n",
       "3            4  female  35.0      1      0  53.1000        S             0   \n",
       "4            5    male  35.0      0      0   8.0500        S             0   \n",
       "\n",
       "   Title_Miss  Title_Mr     ...      Ticket_SOTONO2  Ticket_SOTONOQ  \\\n",
       "0           0         1     ...                   0               0   \n",
       "1           0         0     ...                   0               0   \n",
       "2           1         0     ...                   0               0   \n",
       "3           0         0     ...                   0               0   \n",
       "4           0         1     ...                   0               0   \n",
       "\n",
       "   Ticket_SP  Ticket_STONO  Ticket_STONO2  Ticket_STONOQ  Ticket_SWPP  \\\n",
       "0          0             0              0              0            0   \n",
       "1          0             0              0              0            0   \n",
       "2          0             0              1              0            0   \n",
       "3          0             0              0              0            0   \n",
       "4          0             0              0              0            0   \n",
       "\n",
       "   Ticket_WC  Ticket_WEP  Ticket_XXX  \n",
       "0          0           0           0  \n",
       "1          0           0           0  \n",
       "2          0           0           0  \n",
       "3          0           0           1  \n",
       "4          0           0           1  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier\n",
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_score(clf, X, y, scoring='accuracy'):\n",
    "    xval = cross_val_score(clf, X, y, cv = 5, scoring=scoring)\n",
    "    return np.mean(xval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recover_train_test_target():\n",
    "    global combined\n",
    "    \n",
    "    train0 = pd.read_csv('train.csv')\n",
    "    \n",
    "    targets = train0.Survived\n",
    "    train = combined.head(891)\n",
    "    test = combined.iloc[891:]\n",
    "    \n",
    "    return train, test, targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test, targets = recover_train_test_target()\n",
    "#print(targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files (x86)\\python35\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n",
      "c:\\program files (x86)\\python35\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=50, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here we encode the values before passing it to fit since it does not accept string values\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "for column in train.columns:\n",
    "    if train[column].dtype == type(object):\n",
    "        le = LabelEncoder()\n",
    "        train[column] = train[column].factorize()[0]\n",
    "        train[column] = le.fit_transform(train[column])\n",
    "        \n",
    "for column in test.columns:\n",
    "    if test[column].dtype == type(object):\n",
    "        le = LabelEncoder()\n",
    "        test[column] = test[column].factorize()[0]\n",
    "        test[column] = le.fit_transform(test[column])\n",
    "\n",
    "# Tree-based estimators can be used to compute feature importances, which in turn can be used to discard irrelevant features.\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "clf = RandomForestClassifier(n_estimators=50, max_features='sqrt')\n",
    "clf.fit(train, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "features = pd.DataFrame()\n",
    "features['feature'] = train.columns\n",
    "features['importance'] = clf.feature_importances_\n",
    "features.sort_values(by=['importance'], ascending = True, inplace = True)\n",
    "features.set_index('feature', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25987d9dd68>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25987da6e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we plot the imp of each feature\n",
    "features.plot(kind='barh', figsize=(20, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abhishek Singh\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\__init__.py:54: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`.\n",
      "  if np.issubdtype(mask.dtype, np.int):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(418, 14)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We transform the test and train dataset accordingly\n",
    "model = SelectFromModel(clf, prefit = True)\n",
    "train_red = model.transform(train)\n",
    "test_red = model.transform(test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_gs = False\n",
    "\n",
    "if run_gs:\n",
    "    parameter_grid = {\n",
    "                 'max_depth' : [4, 6, 8],\n",
    "                 'n_estimators': [50, 10],\n",
    "                 'max_features': ['sqrt', 'auto', 'log2'],\n",
    "                 'min_samples_split': [1, 3, 10],\n",
    "                 'min_samples_leaf': [1, 3, 10],\n",
    "                 'bootstrap': [True, False],\n",
    "                 }\n",
    "    forest = RandomForestClassifier()\n",
    "    cross_validation = StratifiedKFold(targets, n_folds=5)\n",
    "\n",
    "    grid_search = GridSearchCV(forest,\n",
    "                               scoring='accuracy',\n",
    "                               param_grid=parameter_grid,\n",
    "                               cv=cross_validation)\n",
    "\n",
    "    grid_search.fit(train, targets)\n",
    "    model = grid_search\n",
    "    parameters = grid_search.best_params_\n",
    "\n",
    "    print('Best score: {}'.format(grid_search.best_score_))\n",
    "    print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "else: \n",
    "    parameters = {'bootstrap': False, 'min_samples_leaf': 3, 'n_estimators': 50, \n",
    "                  'min_samples_split': 10, 'max_features': 'sqrt', 'max_depth': 6}\n",
    "    \n",
    "    model = RandomForestClassifier(**parameters)\n",
    "    model.fit(train, targets)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8025338793320204"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_score(model, train, targets, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = model.predict(test).astype(int)\n",
    "df_output = pd.DataFrame()\n",
    "aux = pd.read_csv('test.csv')\n",
    "df_output['PassengerId'] = aux['PassengerId']\n",
    "df_output['Survived'] = output\n",
    "df_output[['PassengerId','Survived']].to_csv('../../data/output.csv',index=False)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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