01 - Example submission to Kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/UDEA-Esp-Analitica-y-Ciencia-de-Datos/EACD-05-SEMINARIO/blob/master/01%20-%20Example%20submission%20to%20Kaggle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We use [Titanic Data at Kaggle](https://www.kaggle.com/c/titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n"
     ]
    }
   ],
   "source": [
    "d = pd.read_csv(\"train.csv\")\n",
    "print (d.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Understand `NaN` values are present**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         PassengerId 0\n",
      "            Survived 0\n",
      "              Pclass 0\n",
      "                Name 0\n",
      "                 Sex 0\n",
      "                 Age 177\n",
      "               SibSp 0\n",
      "               Parch 0\n",
      "              Ticket 0\n",
      "                Fare 0\n",
      "               Cabin 687\n",
      "            Embarked 2\n"
     ]
    }
   ],
   "source": [
    "for i in d.columns:\n",
    "    print (\"%20s\"%i, np.sum(d[i].isna()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(d.Age.dropna().values, bins=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remove uninformative columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "del(d[\"PassengerId\"])\n",
    "del(d[\"Name\"])\n",
    "del(d[\"Ticket\"])\n",
    "del(d[\"Cabin\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fix `NaN` values**\n",
    "\n",
    "- observe the different filling policies we decide to have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[\"Embarked\"] = d.Embarked.fillna(\"N\")\n",
    "d[\"Age\"]      = d.Age.fillna(d.Age.mean())\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPp0lEQVR4nO3dfYwcd33H8fenCU1LQCSpr5axnTptXZBbFSc9pUFBVSAteaDCQaqiRBVYNJL5w6hJhVQ5VCqgKlKQeGiR2kiGpIQKAimExgoRENxIiEoknIMJfsCNSxxiy7HNY6BIFIdv/9gx2Tjn3MPe3u79/H5Jq535zczO927mPjv725m5VBWSpLb8yqgLkCQtPMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBM4Z7ktVJHkyyJ8nuJDd27e9OcijJzu5xdd8yNyfZn2RfkiuG+QNIkp4vM53nnmQFsKKqHknyUmAHcA1wLfCTqnrfSfOvA+4CLgZeDnwJ+L2qembhy5ckTWfGI/eqOlxVj3TDPwb2AitfYJENwCer6mdV9Tiwn17QS5IWyZlzmTnJGuBC4CHgUuDtSd4CTAHvqKof0Av+r/YtdpAXfjNg2bJltWbNmrmUIkmnvR07dny3qiammzbrcE/yEuAzwE1V9XSS24B/AKp7fj/wV3N4vU3AJoDzzz+fqamp2S4qSQKSPHGqabM6WybJi+gF+8er6h6AqjpSVc9U1S+AD/Ns18shYHXf4qu6tueoqq1VNVlVkxMT077xSJLmaTZnywS4HdhbVR/oa1/RN9ubgF3d8DbguiRnJbkAWAs8vHAlS5JmMptumUuBNwPfTLKza3sncH2S9fS6ZQ4AbwOoqt1J7gb2AMeBzZ4pI0mLa8Zwr6qvAJlm0v0vsMwtwC0D1CVJGoBXqEpSgwx3SWqQ4S5JDTLcJalBhrskNWhOtx+QFtqaLZ+b1XwHbn3DkCuR2uKRuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSg2YM9ySrkzyYZE+S3Ulu7NrPS/JAkse653O79iT5UJL9SR5NctGwfwhJ0nPN5sj9OPCOqloHXAJsTrIO2AJsr6q1wPZuHOAqYG332ATctuBVS5Je0IzhXlWHq+qRbvjHwF5gJbABuLOb7U7gmm54A/Cx6vkqcE6SFQtduCTp1ObU555kDXAh8BCwvKoOd5OeApZ3wyuBJ/sWO9i1SZIWyazDPclLgM8AN1XV0/3TqqqAmsuKk2xKMpVk6tixY3NZVJI0g1mFe5IX0Qv2j1fVPV3zkRPdLd3z0a79ELC6b/FVXdtzVNXWqpqsqsmJiYn51i9JmsZszpYJcDuwt6o+0DdpG7CxG94I3NvX/pburJlLgB/1dd9IkhbBmbOY51LgzcA3k+zs2t4J3ArcneQG4Ang2m7a/cDVwH7gp8BbF7JgSdLMZgz3qvoKkFNMvnya+QvYPGBdkqQBeIWqJDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAbNGO5J7khyNMmuvrZ3JzmUZGf3uLpv2s1J9ifZl+SKYRUuSTq12Ry5fxS4cpr2D1bV+u5xP0CSdcB1wO93y/xLkjMWqlhJ0uzMGO5V9WXg+7N8vQ3AJ6vqZ1X1OLAfuHiA+iRJ8zBIn/vbkzzadduc27WtBJ7sm+dg1yZJWkTzDffbgN8B1gOHgffP9QWSbEoylWTq2LFj8yxDkjSdeYV7VR2pqmeq6hfAh3m26+UQsLpv1lVd23SvsbWqJqtqcmJiYj5lSJJOYV7hnmRF3+ibgBNn0mwDrktyVpILgLXAw4OVKEmaqzNnmiHJXcBlwLIkB4F3AZclWQ8UcAB4G0BV7U5yN7AHOA5srqpnhlK5JOmUZgz3qrp+mubbX2D+W4BbBilKkjQYr1CVpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KAZwz3JHUmOJtnV13ZekgeSPNY9n9u1J8mHkuxP8miSi4ZZvCRperM5cv8ocOVJbVuA7VW1FtjejQNcBaztHpuA2xamTEnSXMwY7lX1ZeD7JzVvAO7shu8Erulr/1j1fBU4J8mKBapVkjRL8+1zX15Vh7vhp4Dl3fBK4Mm++Q52bZKkRTTwF6pVVUDNdbkkm5JMJZk6duzYoGVIkvrMN9yPnOhu6Z6Pdu2HgNV9863q2p6nqrZW1WRVTU5MTMyzDEnSdOYb7tuAjd3wRuDevva3dGfNXAL8qK/7RpK0SM6caYYkdwGXAcuSHATeBdwK3J3kBuAJ4Npu9vuBq4H9wE+Btw6hZknSDGYM96q6/hSTLp9m3gI2D1qUJGkwXqEqSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNWjG/6Gq08OaLZ+b1XwHbn3DkCuRtBA8cpekBhnuktQgw12SGmS4S1KDDHdJapBnyzRstmfAtLZuSR65S1KTDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkho00EVMSQ4APwaeAY5X1WSS84BPAWuAA8C1VfWDwcqUJM3FQhy5v7aq1lfVZDe+BdheVWuB7d24JGkRDaNbZgNwZzd8J3DNENYhSXoBg4Z7AV9MsiPJpq5teVUd7oafApYPuA5J0hwNeuOw11TVoSS/CTyQ5Fv9E6uqktR0C3ZvBpsAzj///AHLkCT1G+jIvaoOdc9Hgc8CFwNHkqwA6J6PnmLZrVU1WVWTExMTg5QhSTrJvMM9ydlJXnpiGHg9sAvYBmzsZtsI3DtokZKkuRmkW2Y58NkkJ17nE1X1+SRfA+5OcgPwBHDt4GVKszPb+8gfuPUNQ65EGq15h3tVfRt41TTt3wMuH6QoSdJgvEJVkhpkuEtSg/wfqpoT/zeqtDR45C5JDTLcJalBdsssQXaNSJqJ4a7T0lzeID0nXkuR3TKS1CDDXZIaZLhLUoPsc9eS4JfI0tx45C5JDTLcJalBS75bxlPaJOn5lny4t8R+ZUkLxXCXFoj/KETjxD53SWqQ4S5JDTLcJalBhrskNchwl6QGebaMNIOFPkXVs2q0GDxyl6QGGe6S1CDDXZIaZJ+7NKbsm9cgDHfpNOGbxenltAr3hd65/WPROPCGc5qOfe6S1CDDXZIadFp1y8yWH3MlLXWGu6R583un8WW4Sxo63wQWn+Eu6TnslmyD4S6pWafzJ4ahhXuSK4F/As4APlJVtw5rXZLacDqH8UIbSrgnOQP4Z+DPgIPA15Jsq6o9w1jfuPNjrqTFNqzz3C8G9lfVt6vq/4BPAhuGtC5J0kmG1S2zEniyb/wg8MdDWpckDWQY3UGj7mIa2ReqSTYBm7rRnyTZN4+XWQZ8d+GqWjDWNXfjWpt1zc2i1JX3zmuxgWub53pnes1B6vqtU00YVrgfAlb3ja/q2n6pqrYCWwdZSZKpqpoc5DWGwbrmblxrs665Gde6YHxrG1Zdw+pz/xqwNskFSX4VuA7YNqR1SZJOMpQj96o6nuTtwBfonQp5R1XtHsa6JEnPN7Q+96q6H7h/WK/fGahbZ4isa+7GtTbrmptxrQvGt7ah1JWqGsbrSpJGyPu5S1KDlmS4J7kyyb4k+5NsGXEtdyQ5mmRXX9t5SR5I8lj3fO4I6lqd5MEke5LsTnLjONSW5NeSPJzkG11d7+naL0jyULdNP9V9Eb/okpyR5OtJ7huzug4k+WaSnUmmurZx2M/OSfLpJN9KsjfJq0ddV5JXdL+nE4+nk9w06rq62v6m2+93Jbmr+3sYyj625MK979YGVwHrgOuTrBthSR8FrjypbQuwvarWAtu78cV2HHhHVa0DLgE2d7+nUdf2M+B1VfUqYD1wZZJLgPcCH6yq3wV+ANywyHWdcCOwt298XOoCeG1Vre87bW7U2xJ694/6fFW9EngVvd/dSOuqqn3d72k98EfAT4HPjrquJCuBvwYmq+oP6J1sch3D2seqakk9gFcDX+gbvxm4ecQ1rQF29Y3vA1Z0wyuAfWPwe7uX3r1+xqY24MXAI/SuXv4ucOZ023gR61lF74/+dcB9QMahrm7dB4BlJ7WNdFsCLwMep/vublzqOqmW1wP/NQ518eyV++fRO5nlPuCKYe1jS+7InelvbbByRLWcyvKqOtwNPwUsH2UxSdYAFwIPMQa1dV0fO4GjwAPA/wA/rKrj3Syj2qb/CPwt8Itu/DfGpC6AAr6YZEd3dTeMflteABwD/rXryvpIkrPHoK5+1wF3dcMjrauqDgHvA74DHAZ+BOxgSPvYUgz3JaV6b8cjOyUpyUuAzwA3VdXT/dNGVVtVPVO9j8yr6N1k7pWLXcPJkvw5cLSqdoy6llN4TVVdRK87cnOSP+mfOKJteSZwEXBbVV0I/C8ndXWMcv/v+q7fCPz7ydNGUVfXx7+B3pviy4GzeX6X7oJZiuE+460NxsCRJCsAuuejoygiyYvoBfvHq+qecaoNoKp+CDxI76PoOUlOXHcxim16KfDGJAfo3cX0dfT6k0ddF/DLoz6q6ii9/uOLGf22PAgcrKqHuvFP0wv7Udd1wlXAI1V1pBsfdV1/CjxeVceq6ufAPfT2u6HsY0sx3JfCrQ22ARu74Y30+rsXVZIAtwN7q+oD41Jbkokk53TDv07ve4C99EL+L0ZVV1XdXFWrqmoNvX3qP6vqL0ddF0CSs5O89MQwvX7kXYx4W1bVU8CTSV7RNV0O7Bl1XX2u59kuGRh9Xd8BLkny4u7v88Tvazj72Ki+6Bjwi4mrgf+m11f7dyOu5S56/Wc/p3ckcwO9vtrtwGPAl4DzRlDXa+h97HwU2Nk9rh51bcAfAl/v6toF/H3X/tvAw8B+eh+jzxrhNr0MuG9c6upq+Eb32H1inx/1tuxqWA9MddvzP4Bzx6Sus4HvAS/raxuHut4DfKvb9/8NOGtY+5hXqEpSg5Zit4wkaQaGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDfp/tUOT2aIdzKUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(d.Age.dropna().values, bins=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Turn categorical columns to a `one_hot` encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 1],\n",
       "       [1, 0, 0, 0],\n",
       "       [0, 0, 0, 1],\n",
       "       [0, 0, 0, 1],\n",
       "       [0, 0, 0, 1]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_onehot(x):\n",
    "    values = np.unique(x)\n",
    "    r = np.r_[[np.argwhere(i==values)[0][0] for i in x]]\n",
    "    return np.eye(len(values))[r].astype(int)\n",
    "    \n",
    "k = to_onehot(d.Embarked.values)\n",
    "k[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_columns_with_onehot(d, col):\n",
    "    k = to_onehot(d[col].values)\n",
    "    r = pd.DataFrame(k, columns=[\"%s_%d\"%(col, i) for i in range(k.shape[1])], index=d.index).join(d)\n",
    "    del(r[col])\n",
    "    return r "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked_0</th>\n",
       "      <th>Embarked_1</th>\n",
       "      <th>Embarked_2</th>\n",
       "      <th>Embarked_3</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Embarked_0  Embarked_1  Embarked_2  Embarked_3  Survived  Pclass     Sex  \\\n",
       "0           0           0           0           1         0       3    male   \n",
       "1           1           0           0           0         1       1  female   \n",
       "2           0           0           0           1         1       3  female   \n",
       "3           0           0           0           1         1       1  female   \n",
       "4           0           0           0           1         0       3    male   \n",
       "\n",
       "    Age  SibSp  Parch     Fare  \n",
       "0  22.0      1      0   7.2500  \n",
       "1  38.0      1      0  71.2833  \n",
       "2  26.0      0      0   7.9250  \n",
       "3  35.0      1      0  53.1000  \n",
       "4  35.0      0      0   8.0500  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = replace_columns_with_onehot(d, \"Embarked\")\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex_0</th>\n",
       "      <th>Sex_1</th>\n",
       "      <th>Embarked_0</th>\n",
       "      <th>Embarked_1</th>\n",
       "      <th>Embarked_2</th>\n",
       "      <th>Embarked_3</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex_0  Sex_1  Embarked_0  Embarked_1  Embarked_2  Embarked_3  Survived  \\\n",
       "0      0      1           0           0           0           1         0   \n",
       "1      1      0           1           0           0           0         1   \n",
       "2      1      0           0           0           0           1         1   \n",
       "3      1      0           0           0           0           1         1   \n",
       "4      0      1           0           0           0           1         0   \n",
       "\n",
       "   Pclass   Age  SibSp  Parch     Fare  \n",
       "0       3  22.0      1      0   7.2500  \n",
       "1       1  38.0      1      0  71.2833  \n",
       "2       3  26.0      0      0   7.9250  \n",
       "3       1  35.0      1      0  53.1000  \n",
       "4       3  35.0      0      0   8.0500  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = replace_columns_with_onehot(d, \"Sex\")\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), 60142.86312352941)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.shape, d.values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Put all transformations together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_titanic(d):\n",
    "    del(d[\"PassengerId\"])\n",
    "    del(d[\"Name\"])\n",
    "    del(d[\"Ticket\"])\n",
    "    del(d[\"Cabin\"])\n",
    "    d[\"Embarked\"] = d.Embarked.fillna(\"N\")\n",
    "    d[\"Fare\"]     = d.Fare.fillna(d.Fare.mean())\n",
    "    d[\"Age\"]      = d.Age.fillna(d.Age.mean())\n",
    "    d = replace_columns_with_onehot(d, \"Embarked\")\n",
    "    d = replace_columns_with_onehot(d, \"Sex\")\n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**transform train and test data together**\n",
    "\n",
    "- observe that test data **does not have** a `Survival` column. This is the result to submit to Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), (418, 11))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtr = pd.read_csv(\"train.csv\")\n",
    "dts = pd.read_csv(\"test.csv\")\n",
    "lentr = len(dtr)\n",
    "dtr.shape, dts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**get data ready for training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 11) (891,)\n",
      "(418, 11)\n"
     ]
    }
   ],
   "source": [
    "source_cols = [i for i in dtr.columns if i!=\"Survived\"]\n",
    "all_data = pd.concat((dtr[source_cols], dts[source_cols]))\n",
    "all_data.index = range(len(all_data))\n",
    "all_data = clean_titanic(all_data)\n",
    "\n",
    "Xtr, ytr = all_data.iloc[:lentr].values, dtr[\"Survived\"].values\n",
    "Xts      = all_data.iloc[lentr:].values\n",
    "\n",
    "print (Xtr.shape, ytr.shape)\n",
    "print (Xts.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**cross validate for model selection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.79329609 0.79775281 0.85955056 0.76966292 0.83707865]\n",
      "[0.59217877 0.71348315 0.69101124 0.68539326 0.69101124]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "rf = RandomForestClassifier()\n",
    "print (cross_val_score(rf, Xtr, ytr))\n",
    "\n",
    "svc = SVC()\n",
    "print (cross_val_score(svc, Xtr, ytr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**now train with full dataset and generate submission for Kaggle**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(Xtr, ytr)\n",
    "preds_ts = rf.predict(Xts)\n",
    "preds_ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**get predictions ready to submit to Kaggle**\n",
    "\n",
    "- see https://www.kaggle.com/c/titanic#evaluation for file format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         1\n",
       "4          896         1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame([dts.PassengerId, pd.Series(preds_ts, name=\"Survived\")]).T\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"titanic_kaggle.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId,Survived\r\n",
      "892,0\r\n",
      "893,0\r\n",
      "894,0\r\n",
      "895,1\r\n",
      "896,1\r\n",
      "897,0\r\n",
      "898,0\r\n",
      "899,0\r\n",
      "900,1\r\n"
     ]
    }
   ],
   "source": [
    "!head titanic_kaggle.csv"
   ]
  }
 ],
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