Titanic-solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <td>S</td>\n",
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       "    <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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      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  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('titanic.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gérer les données manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(df.median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capturer les features d'intérêt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cabin_letter(x):\n",
    "    if type(x) is str:\n",
    "        return x[0]\n",
    "    else:\n",
    "        return ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
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       "    <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",
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       "      <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",
       "      <td>C</td>\n",
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       "    <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",
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       "      <th>886</th>\n",
       "      <td>0</td>\n",
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       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\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   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   \n",
       "888         0       3  female  28.0      1      2  23.4500        S   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   \n",
       "\n",
       "    Cabin_letter  \n",
       "0                 \n",
       "1              C  \n",
       "2                 \n",
       "3              C  \n",
       "4                 \n",
       "..           ...  \n",
       "886               \n",
       "887            B  \n",
       "888               \n",
       "889            C  \n",
       "890               \n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin_letter'] = df.Cabin.apply(get_cabin_letter)\n",
    "df = df.drop(columns=['PassengerId', 'Name', 'Ticket', 'Cabin'], errors='ignore')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gérer les données catégoriques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Cabin_letter_A</th>\n",
       "      <th>Cabin_letter_B</th>\n",
       "      <th>Cabin_letter_C</th>\n",
       "      <th>Cabin_letter_D</th>\n",
       "      <th>Cabin_letter_E</th>\n",
       "      <th>Cabin_letter_F</th>\n",
       "      <th>Cabin_letter_G</th>\n",
       "      <th>Cabin_letter_T</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived   Age  SibSp  Parch     Fare  Embarked_Q  Embarked_S  Sex_male  \\\n",
       "0           0  22.0      1      0   7.2500           0           1         1   \n",
       "1           1  38.0      1      0  71.2833           0           0         0   \n",
       "2           1  26.0      0      0   7.9250           0           1         0   \n",
       "3           1  35.0      1      0  53.1000           0           1         0   \n",
       "4           0  35.0      0      0   8.0500           0           1         1   \n",
       "..        ...   ...    ...    ...      ...         ...         ...       ...   \n",
       "886         0  27.0      0      0  13.0000           0           1         1   \n",
       "887         1  19.0      0      0  30.0000           0           1         0   \n",
       "888         0  28.0      1      2  23.4500           0           1         0   \n",
       "889         1  26.0      0      0  30.0000           0           0         1   \n",
       "890         0  32.0      0      0   7.7500           1           0         1   \n",
       "\n",
       "     Cabin_letter_A  Cabin_letter_B  Cabin_letter_C  Cabin_letter_D  \\\n",
       "0                 0               0               0               0   \n",
       "1                 0               0               1               0   \n",
       "2                 0               0               0               0   \n",
       "3                 0               0               1               0   \n",
       "4                 0               0               0               0   \n",
       "..              ...             ...             ...             ...   \n",
       "886               0               0               0               0   \n",
       "887               0               1               0               0   \n",
       "888               0               0               0               0   \n",
       "889               0               0               1               0   \n",
       "890               0               0               0               0   \n",
       "\n",
       "     Cabin_letter_E  Cabin_letter_F  Cabin_letter_G  Cabin_letter_T  Pclass_2  \\\n",
       "0                 0               0               0               0         0   \n",
       "1                 0               0               0               0         0   \n",
       "2                 0               0               0               0         0   \n",
       "3                 0               0               0               0         0   \n",
       "4                 0               0               0               0         0   \n",
       "..              ...             ...             ...             ...       ...   \n",
       "886               0               0               0               0         1   \n",
       "887               0               0               0               0         0   \n",
       "888               0               0               0               0         0   \n",
       "889               0               0               0               0         0   \n",
       "890               0               0               0               0         0   \n",
       "\n",
       "     Pclass_3  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           0  \n",
       "4           1  \n",
       "..        ...  \n",
       "886         0  \n",
       "887         0  \n",
       "888         1  \n",
       "889         0  \n",
       "890         1  \n",
       "\n",
       "[891 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=['Embarked', 'Sex', 'Cabin_letter', 'Pclass'], drop_first=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Séparer les données en train et test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, df.columns != 'Survived']\n",
    "y = df.loc[:, df.columns == 'Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=df.Survived)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardiser les données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_std = X_train[:]\n",
    "mean = X_train[['Age', 'Fare']].mean()\n",
    "std = X_train[['Age', 'Fare']].std()\n",
    "X_train_std[['Age', 'Fare']] = (X_train[['Age', 'Fare']] - mean)/std\n",
    "\n",
    "X_test_std = X_test[:]\n",
    "X_test_std[['Age', 'Fare']] = (X_test[['Age', 'Fare']] - mean)/std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entrainer les modèles de votre choix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "models = [ LogisticRegression(max_iter=10_000),\n",
    "            SGDClassifier(penalty='elasticnet', fit_intercept=False),\n",
    "            SVC(),\n",
    "            DecisionTreeClassifier(),\n",
    "            RandomForestClassifier(),\n",
    "            KNeighborsClassifier(),\n",
    "         ]\n",
    "\n",
    "for model in models:\n",
    "    model.fit(X_train_std, y_train.iloc[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparer leurs performances "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=10000,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n",
      "0.7892376681614349\n",
      "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
      "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=False,\n",
      "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
      "              max_iter=1000, n_iter_no_change=5, n_jobs=None,\n",
      "              penalty='elasticnet', power_t=0.5, random_state=None,\n",
      "              shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\n",
      "              warm_start=False)\n",
      "0.757847533632287\n",
      "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
      "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False)\n",
      "0.8116591928251121\n",
      "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
      "                       random_state=None, splitter='best')\n",
      "0.7802690582959642\n",
      "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
      "                       criterion='gini', max_depth=None, max_features='auto',\n",
      "                       max_leaf_nodes=None, max_samples=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=0, warm_start=False)\n",
      "0.7668161434977578\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "                     weights='uniform')\n",
      "0.7713004484304933\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    print(model)\n",
    "    print(accuracy_score(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quelles sont les features les plus intéressantes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fare              0.252059\n",
       "Sex_male          0.245863\n",
       "Age               0.235734\n",
       "Pclass_3          0.061176\n",
       "SibSp             0.048114\n",
       "Parch             0.041786\n",
       "Embarked_S        0.024844\n",
       "Cabin_letter_D    0.019158\n",
       "Pclass_2          0.015593\n",
       "Cabin_letter_E    0.014671\n",
       "Cabin_letter_C    0.011691\n",
       "Embarked_Q        0.010633\n",
       "Cabin_letter_B    0.007828\n",
       "Cabin_letter_A    0.004157\n",
       "Cabin_letter_F    0.004002\n",
       "Cabin_letter_G    0.002258\n",
       "Cabin_letter_T    0.000434\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(models[-2].feature_importances_, index=X_train_std.columns).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# De quelle façon le sexe affecte-t-il le taux de survie?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import plot_partial_dependence\n",
    "plot_partial_dependence(models[-2], X_train_std, ['Sex_male'], target=1, n_jobs=-1) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# De quelle façon la classe (Pclass) affecte-t-elle le taux de survie? et les autres features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alexisfortin-cote/miniconda3/envs/data-science/lib/python3.7/site-packages/sklearn/inspection/_partial_dependence.py:715: FutureWarning: The fig parameter is deprecated in version 0.22 and will be removed in version 0.24\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,20))\n",
    "plot_partial_dependence(models[-2], X_train_std,X_train_std.columns, target=1, n_jobs=-1, fig=fig) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.3 64-bit ('data-science': conda)",
   "language": "python",
   "name": "python37364bitdatascienceconda21efd4ba96a746aa869a1d54068eadd9"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}