titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Permet de mettre l'ensemble des données dans une variable\n",
    "train = pd.read_csv(\"data/train.csv\")\n",
    "#Affiche les 10 premiers valeurs\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId    891\n",
      "Survived       891\n",
      "Pclass         891\n",
      "Name           891\n",
      "Sex            891\n",
      "Age            714\n",
      "SibSp          891\n",
      "Parch          891\n",
      "Ticket         891\n",
      "Fare           891\n",
      "Cabin          204\n",
      "Embarked       889\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Affiche le nombre de donnée par variable\n",
    "print(train.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre de lignes: 891\n",
      "Nombre de colonnes: 12\n",
      "Colonnes: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked\n"
     ]
    }
   ],
   "source": [
    "#shape renvoie la dimension de la matrice (2,2), shape[0] = premiere dimension, shape[1] = deuxieme dimension\n",
    "total = train.shape[0]\n",
    "print(\"Nombre de lignes:\", train.shape[0])\n",
    "print(\"Nombre de colonnes:\", train.shape[1])\n",
    "#train.columns renvoie un String avec les noms des colonnes\n",
    "print(\"Colonnes:\", \", \".join(train.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age  Cabin  Embarked\n",
      "0  177    687         2\n"
     ]
    }
   ],
   "source": [
    "#renvoie un tableau avec le nombre d'élément manquant par variable\n",
    "def df_manquant(df):\n",
    "    count = df.count()\n",
    "    result = {}\n",
    "    for key in df.keys():\n",
    "        left = total - count[key]\n",
    "        if( left > 0 ):\n",
    "            result[key] = left\n",
    "    return pd.DataFrame(result, index=[0])\n",
    "\n",
    "result1 = df_manquant(train)\n",
    "print(result1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre de survivants: 342\n",
      "Nombre de morts: 549\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f730e430f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Deux maniere de trouver le nombre de survivant ou de mort\n",
    "#1\n",
    "print(\"Nombre de survivants:\",train[\"Survived\"].value_counts()[1])\n",
    "#2\n",
    "print(\"Nombre de morts:\",len(train[train[\"Survived\"]==0]))\n",
    "\n",
    "#fonction qui permet d'avoir un graphique d'une dataFrame et d'une varible\n",
    "def plot(df, feature):\n",
    "    survived = train[train[\"Survived\"]==1]\n",
    "    dead = train[train[\"Survived\"]==0]\n",
    "    x1 = np.array(dead[feature])\n",
    "    x2 = np.array(survived[feature])\n",
    "    plt.hist([x1, x2], color=['r', 'g'])\n",
    "    plt.title('Distribution de ' + feature)\n",
    "    plt.show()\n",
    "plot(train, 'Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#remplace les valeurs vides par la median, et pour les strings, la valeur la plus récurrente\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le_sex = LabelEncoder()\n",
    "train['sex_encoded'] = le_sex.fit_transform(train.Sex)\n",
    "median = train.median()\n",
    "train[\"Age\"].fillna(median[\"Age\"], inplace=True)\n",
    "train[\"Cabin\"].fillna(train[\"Cabin\"].mode()[0], inplace=True)\n",
    "train[\"Embarked\"].fillna(train[\"Embarked\"].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>sex_encoded</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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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",
       "      <td>0</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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>0</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",
       "      <td>0</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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male  28.0      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare    Cabin Embarked  sex_encoded  \n",
       "0      0         A/5 21171   7.2500  B96 B98        S            1  \n",
       "1      0          PC 17599  71.2833      C85        C            0  \n",
       "2      0  STON/O2. 3101282   7.9250  B96 B98        S            0  \n",
       "3      0            113803  53.1000     C123        S            0  \n",
       "4      0            373450   8.0500  B96 B98        S            1  \n",
       "5      0            330877   8.4583  B96 B98        Q            1  \n",
       "6      0             17463  51.8625      E46        S            1  \n",
       "7      1            349909  21.0750  B96 B98        S            1  \n",
       "8      2            347742  11.1333  B96 B98        S            0  \n",
       "9      0            237736  30.0708  B96 B98        C            0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verification\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [0]\n"
     ]
    }
   ],
   "source": [
    "#Test s'il y a encore des valeurs vides\n",
    "result2 = df_manquant(train)\n",
    "print(result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId    891\n",
      "Survived       891\n",
      "Pclass         891\n",
      "Name           891\n",
      "Sex            891\n",
      "Age            891\n",
      "SibSp          891\n",
      "Parch          891\n",
      "Ticket         891\n",
      "Fare           891\n",
      "Cabin          891\n",
      "Embarked       891\n",
      "sex_encoded    891\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Verification\n",
    "print(train.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keeping: SibSp, Parch, Age\n",
      "[0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116, 0.61111111111111116]\n",
      "Accuracy: 61.11%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "#keep = list(train.keys())\n",
    "#keep.remove(\"Survived\")\n",
    "#ep = [\"SibSp\", \"Parch\", \"Fare\", \"sex_encoded\"]\n",
    "#keep = [\"Pclass\", \"Parch\", \"SibSp\"]\n",
    "#keep = [\"Pclass\", \"Age\", \"Fare\"]\n",
    "#keep = [\"SibSp\",, \"Parch\" \"Age\", \"Fare\"]\n",
    "keep = [\"SibSp\", \"Parch\" ,\"Age\"]\n",
    "def df_score(df, keep, division):\n",
    "    print(\"Keeping:\", \", \".join(keep))\n",
    "    list_test_size = [a/division for a in list(range(division))][1:]\n",
    "    scores = []\n",
    "    for ts in list_test_size:\n",
    "        X_train, X_test, y_train, y_test = train_test_split(df[keep], df[\"Survived\"], test_size=0.1, random_state=0)\n",
    "        logreg = LogisticRegression()\n",
    "        logreg.fit(X_train, y_train)\n",
    "        y_pred = logreg.predict(X_test)\n",
    "        scores.append(logreg.score(X_test, y_test))\n",
    "    return scores\n",
    "scores = df_score(train,keep, 20)\n",
    "print(scores)\n",
    "print('Accuracy: {:.2f}%'.format(np.array(scores).mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pclass_1  Pclass_2  Pclass_3\n",
      "0         0.0       0.0       1.0\n",
      "1         1.0       0.0       0.0\n",
      "2         0.0       0.0       1.0\n",
      "3         1.0       0.0       0.0\n",
      "4         0.0       0.0       1.0\n",
      "5         0.0       0.0       1.0\n",
      "6         1.0       0.0       0.0\n",
      "7         0.0       0.0       1.0\n",
      "8         0.0       0.0       1.0\n",
      "9         0.0       1.0       0.0\n",
      "10        0.0       0.0       1.0\n",
      "11        1.0       0.0       0.0\n",
      "12        0.0       0.0       1.0\n",
      "13        0.0       0.0       1.0\n",
      "14        0.0       0.0       1.0\n",
      "15        0.0       1.0       0.0\n",
      "16        0.0       0.0       1.0\n",
      "17        0.0       1.0       0.0\n",
      "18        0.0       0.0       1.0\n",
      "19        0.0       0.0       1.0\n",
      "20        0.0       1.0       0.0\n",
      "21        0.0       1.0       0.0\n",
      "22        0.0       0.0       1.0\n",
      "23        1.0       0.0       0.0\n",
      "24        0.0       0.0       1.0\n",
      "25        0.0       0.0       1.0\n",
      "26        0.0       0.0       1.0\n",
      "27        1.0       0.0       0.0\n",
      "28        0.0       0.0       1.0\n",
      "29        0.0       0.0       1.0\n",
      "..        ...       ...       ...\n",
      "861       0.0       1.0       0.0\n",
      "862       1.0       0.0       0.0\n",
      "863       0.0       0.0       1.0\n",
      "864       0.0       1.0       0.0\n",
      "865       0.0       1.0       0.0\n",
      "866       0.0       1.0       0.0\n",
      "867       1.0       0.0       0.0\n",
      "868       0.0       0.0       1.0\n",
      "869       0.0       0.0       1.0\n",
      "870       0.0       0.0       1.0\n",
      "871       1.0       0.0       0.0\n",
      "872       1.0       0.0       0.0\n",
      "873       0.0       0.0       1.0\n",
      "874       0.0       1.0       0.0\n",
      "875       0.0       0.0       1.0\n",
      "876       0.0       0.0       1.0\n",
      "877       0.0       0.0       1.0\n",
      "878       0.0       0.0       1.0\n",
      "879       1.0       0.0       0.0\n",
      "880       0.0       1.0       0.0\n",
      "881       0.0       0.0       1.0\n",
      "882       0.0       0.0       1.0\n",
      "883       0.0       1.0       0.0\n",
      "884       0.0       0.0       1.0\n",
      "885       0.0       0.0       1.0\n",
      "886       0.0       1.0       0.0\n",
      "887       1.0       0.0       0.0\n",
      "888       0.0       0.0       1.0\n",
      "889       1.0       0.0       0.0\n",
      "890       0.0       0.0       1.0\n",
      "\n",
      "[891 rows x 3 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:561: 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.obj[item_labels[indexer[info_axis]]] = value\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "def dummify_features(df):\n",
    "    colnames = df.columns\n",
    "    le_dict = {}\n",
    "    for col in colnames:\n",
    "        le_dict[col] = preprocessing.LabelEncoder()\n",
    "        le_dict[col].fit(df[col])\n",
    "        df.loc[:, col] = le_dict[col].transform(df[col])\n",
    "    enc = preprocessing.OneHotEncoder()\n",
    "    enc.fit(df)\n",
    "    X = enc.transform(df)\n",
    "    dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\n",
    "    # for cv in colnames:\n",
    "    #     for modality in le_dict[cv].classes_:\n",
    "    #         dummy_colnames.append(cv + '_' + modality)\n",
    "    return X, dummy_colnames, enc\n",
    "\n",
    "to_dummify = [\"Pclass\"]\n",
    "\n",
    "X_dummy, dummy_columns, dummy_encoder = dummify_features(train[to_dummify])\n",
    "X_dummy = pd.DataFrame(X_dummy.toarray(), columns=dummy_columns)\n",
    "\n",
    "print(X_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "echantillon = X_dummy.join(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keeping: SibSp, Parch, Fare, Pclass_1, Pclass_2, Pclass_3, sex_encoded\n",
      "[0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219, 0.82222222222222219]\n",
      "Accuracy: 82.22%\n"
     ]
    }
   ],
   "source": [
    "keep = [\"SibSp\", \"Parch\" ,\"Fare\", \"Pclass_1\",\"Pclass_2\",\"Pclass_3\", \"sex_encoded\"]\n",
    "scores = df_score(echantillon, keep, 20)\n",
    "print(scores)\n",
    "print('Accuracy: {:.2f}%'.format(np.array(scores).mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7307556518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#survived = train[train[\"Survived\"]==1]\n",
    "#dead = train[train[\"Survived\"]==0]\n",
    "#x1 = np.array(dead['Age'])\n",
    "#x2 = np.array(survived['Age'])\n",
    "#plt.hist([x1, x2], color=['r', 'g'])\n",
    "#plt.title('Distribution de ' + 'Age')\n",
    "#plt.show()\n",
    "plot(train, 'Age')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keeping: SibSp, Parch, Pclass_1, Pclass_2, Pclass_3, sex_encoded, Age, is_child\n",
      "[0.81111111111111112]\n",
      "Accuracy: 81.11%\n"
     ]
    }
   ],
   "source": [
    "echantillon['is_child'] = echantillon[\"Age\"] < 8\n",
    "cols = [c for c in echantillon.columns if 'Age' not in c]\n",
    "echantillon1 = echantillon[cols]\n",
    "\n",
    "keep = [\"SibSp\", \"Parch\", \"Pclass_1\",\"Pclass_2\",\"Pclass_3\", \"sex_encoded\",\"Age\", \"is_child\"]\n",
    "scores = df_score(echantillon, keep, 2)\n",
    "print(scores)\n",
    "print('Accuracy: {:.2f}%'.format(np.array(scores).mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</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",
       "      <th>sex_encoded</th>\n",
       "      <th>is_child</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\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",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\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",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass_1  Pclass_2  Pclass_3  PassengerId  Survived  Pclass  \\\n",
       "0       0.0       0.0       1.0            1         0       3   \n",
       "1       1.0       0.0       0.0            2         1       1   \n",
       "2       0.0       0.0       1.0            3         1       3   \n",
       "3       1.0       0.0       0.0            4         1       1   \n",
       "4       0.0       0.0       1.0            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  sex_encoded is_child  \n",
       "0      0         A/5 21171   7.2500  B96 B98        S            1    False  \n",
       "1      0          PC 17599  71.2833      C85        C            0    False  \n",
       "2      0  STON/O2. 3101282   7.9250  B96 B98        S            0    False  \n",
       "3      0            113803  53.1000     C123        S            0    False  \n",
       "4      0            373450   8.0500  B96 B98        S            1    False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echantillon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "echantillon['Title'] = echantillon.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "echantillon['Title'] = echantillon['Title'].replace(\n",
    "    ['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], \n",
    "    'Rare'\n",
    ")\n",
    "\n",
    "echantillon['Title'] = echantillon['Title'].replace('Mlle', 'Miss')\n",
    "echantillon['Title'] = echantillon['Title'].replace('Ms', 'Miss')\n",
    "echantillon['Title'] = echantillon['Title'].replace('Mme', 'Mrs')\n",
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "echantillon['Title'] = echantillon['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</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",
       "      <th>sex_encoded</th>\n",
       "      <th>is_child</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\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",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\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",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass_1  Pclass_2  Pclass_3  PassengerId  Survived  Pclass  \\\n",
       "0       0.0       0.0       1.0            1         0       3   \n",
       "1       1.0       0.0       0.0            2         1       1   \n",
       "2       0.0       0.0       1.0            3         1       3   \n",
       "3       1.0       0.0       0.0            4         1       1   \n",
       "4       0.0       0.0       1.0            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  sex_encoded is_child  \\\n",
       "0      0         A/5 21171   7.2500  B96 B98        S            1    False   \n",
       "1      0          PC 17599  71.2833      C85        C            0    False   \n",
       "2      0  STON/O2. 3101282   7.9250  B96 B98        S            0    False   \n",
       "3      0            113803  53.1000     C123        S            0    False   \n",
       "4      0            373450   8.0500  B96 B98        S            1    False   \n",
       "\n",
       "   Title  \n",
       "0      1  \n",
       "1      3  \n",
       "2      2  \n",
       "3      3  \n",
       "4      1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echantillon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keeping: SibSp, Parch, Pclass_1, Pclass_2, Pclass_3, sex_encoded, Age, is_child\n",
      "[0.81111111111111112]\n",
      "Accuracy: 81.11%\n"
     ]
    }
   ],
   "source": [
    "keep = [\"SibSp\", \"Parch\", \"Pclass_1\",\"Pclass_2\",\"Pclass_3\", \"sex_encoded\",\"Age\", \"is_child\"]\n",
    "scores = df_score(echantillon, keep, 2)\n",
    "print(scores)\n",
    "print('Accuracy: {:.2f}%'.format(np.array(scores).mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Title_1  Title_2  Title_3  Title_4  Title_5\n",
      "0        1.0      0.0      0.0      0.0      0.0\n",
      "1        0.0      0.0      1.0      0.0      0.0\n",
      "2        0.0      1.0      0.0      0.0      0.0\n",
      "3        0.0      0.0      1.0      0.0      0.0\n",
      "4        1.0      0.0      0.0      0.0      0.0\n",
      "5        1.0      0.0      0.0      0.0      0.0\n",
      "6        1.0      0.0      0.0      0.0      0.0\n",
      "7        0.0      0.0      0.0      1.0      0.0\n",
      "8        0.0      0.0      1.0      0.0      0.0\n",
      "9        0.0      0.0      1.0      0.0      0.0\n",
      "10       0.0      1.0      0.0      0.0      0.0\n",
      "11       0.0      1.0      0.0      0.0      0.0\n",
      "12       1.0      0.0      0.0      0.0      0.0\n",
      "13       1.0      0.0      0.0      0.0      0.0\n",
      "14       0.0      1.0      0.0      0.0      0.0\n",
      "15       0.0      0.0      1.0      0.0      0.0\n",
      "16       0.0      0.0      0.0      1.0      0.0\n",
      "17       1.0      0.0      0.0      0.0      0.0\n",
      "18       0.0      0.0      1.0      0.0      0.0\n",
      "19       0.0      0.0      1.0      0.0      0.0\n",
      "20       1.0      0.0      0.0      0.0      0.0\n",
      "21       1.0      0.0      0.0      0.0      0.0\n",
      "22       0.0      1.0      0.0      0.0      0.0\n",
      "23       1.0      0.0      0.0      0.0      0.0\n",
      "24       0.0      1.0      0.0      0.0      0.0\n",
      "25       0.0      0.0      1.0      0.0      0.0\n",
      "26       1.0      0.0      0.0      0.0      0.0\n",
      "27       1.0      0.0      0.0      0.0      0.0\n",
      "28       0.0      1.0      0.0      0.0      0.0\n",
      "29       1.0      0.0      0.0      0.0      0.0\n",
      "..       ...      ...      ...      ...      ...\n",
      "861      1.0      0.0      0.0      0.0      0.0\n",
      "862      0.0      0.0      1.0      0.0      0.0\n",
      "863      0.0      1.0      0.0      0.0      0.0\n",
      "864      1.0      0.0      0.0      0.0      0.0\n",
      "865      0.0      0.0      1.0      0.0      0.0\n",
      "866      0.0      1.0      0.0      0.0      0.0\n",
      "867      1.0      0.0      0.0      0.0      0.0\n",
      "868      1.0      0.0      0.0      0.0      0.0\n",
      "869      0.0      0.0      0.0      1.0      0.0\n",
      "870      1.0      0.0      0.0      0.0      0.0\n",
      "871      0.0      0.0      1.0      0.0      0.0\n",
      "872      1.0      0.0      0.0      0.0      0.0\n",
      "873      1.0      0.0      0.0      0.0      0.0\n",
      "874      0.0      0.0      1.0      0.0      0.0\n",
      "875      0.0      1.0      0.0      0.0      0.0\n",
      "876      1.0      0.0      0.0      0.0      0.0\n",
      "877      1.0      0.0      0.0      0.0      0.0\n",
      "878      1.0      0.0      0.0      0.0      0.0\n",
      "879      0.0      0.0      1.0      0.0      0.0\n",
      "880      0.0      0.0      1.0      0.0      0.0\n",
      "881      1.0      0.0      0.0      0.0      0.0\n",
      "882      0.0      1.0      0.0      0.0      0.0\n",
      "883      1.0      0.0      0.0      0.0      0.0\n",
      "884      1.0      0.0      0.0      0.0      0.0\n",
      "885      0.0      0.0      1.0      0.0      0.0\n",
      "886      0.0      0.0      0.0      0.0      1.0\n",
      "887      0.0      1.0      0.0      0.0      0.0\n",
      "888      0.0      1.0      0.0      0.0      0.0\n",
      "889      1.0      0.0      0.0      0.0      0.0\n",
      "890      1.0      0.0      0.0      0.0      0.0\n",
      "\n",
      "[891 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/pandas/core/indexing.py:561: 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.obj[item_labels[indexer[info_axis]]] = value\n"
     ]
    }
   ],
   "source": [
    "to_dummify = [\"Title\"]\n",
    "\n",
    "X_dummyTitle, dummy_columnsTitle, dummy_encoderTitle = dummify_features(echantillon[to_dummify])\n",
    "X_dummyTitle = pd.DataFrame(X_dummyTitle.toarray(), columns=dummy_columnsTitle)\n",
    "\n",
    "print(X_dummyTitle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "echantillon = X_dummyTitle.join(echantillon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keeping: SibSp, Parch, Fare, Pclass_1, Pclass_2, Pclass_3, sex_encoded, is_child, Title_1, Title_2, Title_3, Title_4, Title_5\n",
      "[0.84444444444444444, 0.84444444444444444, 0.84444444444444444, 0.84444444444444444]\n",
      "Accuracy: 84.44%\n"
     ]
    }
   ],
   "source": [
    "keep = [\"SibSp\", \"Parch\",\"Fare\", \"Pclass_1\",\"Pclass_2\",\"Pclass_3\", \"sex_encoded\", \"is_child\",\"Title_1\",\"Title_2\",\"Title_3\",\"Title_4\",\"Title_5\"]\n",
    "scores = df_score(echantillon, keep, 5)\n",
    "print(scores)\n",
    "print('Accuracy: {:.2f}%'.format(np.array(scores).mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Title_1  Title_2  Title_3  Title_4  Title_5  Pclass_1  Pclass_2  Pclass_3  \\\n",
       "0      1.0      0.0      0.0      0.0      0.0       0.0       0.0       1.0   \n",
       "1      0.0      0.0      1.0      0.0      0.0       1.0       0.0       0.0   \n",
       "2      0.0      1.0      0.0      0.0      0.0       0.0       0.0       1.0   \n",
       "3      0.0      0.0      1.0      0.0      0.0       1.0       0.0       0.0   \n",
       "4      1.0      0.0      0.0      0.0      0.0       0.0       0.0       1.0   \n",
       "\n",
       "   PassengerId  Survived  ...    Age SibSp Parch            Ticket     Fare  \\\n",
       "0            1         0  ...   22.0     1     0         A/5 21171   7.2500   \n",
       "1            2         1  ...   38.0     1     0          PC 17599  71.2833   \n",
       "2            3         1  ...   26.0     0     0  STON/O2. 3101282   7.9250   \n",
       "3            4         1  ...   35.0     1     0            113803  53.1000   \n",
       "4            5         0  ...   35.0     0     0            373450   8.0500   \n",
       "\n",
       "     Cabin Embarked  sex_encoded is_child Title  \n",
       "0  B96 B98        S            1    False     1  \n",
       "1      C85        C            0    False     3  \n",
       "2  B96 B98        S            0    False     2  \n",
       "3     C123        S            0    False     3  \n",
       "4  B96 B98        S            1    False     1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echantillon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "echantillon['FamilySize'] = echantillon[\"SibSp\"] + echantillon[\"Parch\"] + 1\n",
    "echantillon[\"Embarked_encoded\"] = echantillon[\"Embarked\"].map(dict(zip((\"S\", \"C\", \"Q\"), (0, 1, 2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title_1</th>\n",
       "      <th>Title_2</th>\n",
       "      <th>Title_3</th>\n",
       "      <th>Title_4</th>\n",
       "      <th>Title_5</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>...</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>sex_encoded</th>\n",
       "      <th>is_child</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Embarked_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Title_1  Title_2  Title_3  Title_4  Title_5  Pclass_1  Pclass_2  Pclass_3  \\\n",
       "0      1.0      0.0      0.0      0.0      0.0       0.0       0.0       1.0   \n",
       "1      0.0      0.0      1.0      0.0      0.0       1.0       0.0       0.0   \n",
       "2      0.0      1.0      0.0      0.0      0.0       0.0       0.0       1.0   \n",
       "3      0.0      0.0      1.0      0.0      0.0       1.0       0.0       0.0   \n",
       "4      1.0      0.0      0.0      0.0      0.0       0.0       0.0       1.0   \n",
       "\n",
       "   PassengerId  Survived       ...         Parch            Ticket     Fare  \\\n",
       "0            1         0       ...             0         A/5 21171   7.2500   \n",
       "1            2         1       ...             0          PC 17599  71.2833   \n",
       "2            3         1       ...             0  STON/O2. 3101282   7.9250   \n",
       "3            4         1       ...             0            113803  53.1000   \n",
       "4            5         0       ...             0            373450   8.0500   \n",
       "\n",
       "     Cabin  Embarked  sex_encoded is_child  Title FamilySize Embarked_encoded  \n",
       "0  B96 B98         S            1    False      1          2                0  \n",
       "1      C85         C            0    False      3          2                1  \n",
       "2  B96 B98         S            0    False      2          1                0  \n",
       "3     C123         S            0    False      3          2                0  \n",
       "4  B96 B98         S            1    False      1          1                0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echantillon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "predictors = [\"SibSp\", \"Parch\",\"Fare\", \"Pclass_1\",\"Pclass_2\",\"Pclass_3\", \"sex_encoded\", \"is_child\",\"Title_1\",\"Title_2\",\"Title_3\",\"Title_4\",\"Title_5\",\"FamilySize\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(echantillon[predictors], echantillon[\"Survived\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest score: 84.30%\n"
     ]
    }
   ],
   "source": [
    "forest = RandomForestClassifier(n_estimators=100,\n",
    "                                criterion='gini',\n",
    "                                max_depth=5,\n",
    "                                min_samples_split=80,\n",
    "                                min_samples_leaf=5,\n",
    "                                random_state=0)\n",
    "forest.fit(X_train, y_train)\n",
    "print(\"Random Forest score: {:.2f}%\".format(forest.score(X_test, y_test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def random_range(df):\n",
    "    RandomForestClassifier(n_estimators=100,\n",
    "                                criterion='gini',\n",
    "                                max_depth=5,\n",
    "                                min_samples_split=80,\n",
    "                                min_samples_leaf=5,\n",
    "                                random_state=0)\n",
    "    forest.fit(X_train, y_train)\n",
    "\n",
    "    return X, dummy_colnames, enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keeping: SibSp, Parch, Fare, Pclass_1, Pclass_2, Pclass_3, sex_encoded, is_child, Title_1, Title_2, Title_3, Title_4, Title_5, FamilySize, Embarked_encoded\n",
      "[0.83333333333333337, 0.83333333333333337, 0.83333333333333337, 0.83333333333333337]\n",
      "Accuracy: 83.33%\n"
     ]
    }
   ],
   "source": [
    "keep = [\"SibSp\", \"Parch\",\"Fare\", \"Pclass_1\",\"Pclass_2\",\"Pclass_3\", \"sex_encoded\", \"is_child\",\"Title_1\",\"Title_2\",\"Title_3\",\"Title_4\",\"Title_5\",\"FamilySize\",\"Embarked_encoded\"]\n",
    "scores = df_score(echantillon, keep, 5)\n",
    "print(scores)\n",
    "print('Accuracy: {:.2f}%'.format(np.array(scores).mean()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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