Decision Tree.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression on Titanic Dataset\n",
    "#\n",
    "#### We try to predict with linear if the passenger of the Titanic boat will survive or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n"
   ]
  },
  {
   "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",
       "      <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>...</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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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('../train.csv', header=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prétraitement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\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",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>890</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked_C  \\\n",
       "0           0       3    0  22.0      1      0   7.2500           0   \n",
       "1           1       1    1  38.0      1      0  71.2833           1   \n",
       "2           1       3    1  26.0      0      0   7.9250           0   \n",
       "3           1       1    1  35.0      1      0  53.1000           0   \n",
       "4           0       3    0  35.0      0      0   8.0500           0   \n",
       "..        ...     ...  ...   ...    ...    ...      ...         ...   \n",
       "886         0       2    0  27.0      0      0  13.0000           0   \n",
       "887         1       1    1  19.0      0      0  30.0000           0   \n",
       "888         0       3    1  29.0      1      2  23.4500           0   \n",
       "889         1       1    0  26.0      0      0  30.0000           1   \n",
       "890         0       3    0  32.0      0      0   7.7500           0   \n",
       "\n",
       "     Embarked_Q  Embarked_S  \n",
       "0             0           1  \n",
       "1             0           0  \n",
       "2             0           1  \n",
       "3             0           1  \n",
       "4             0           1  \n",
       "..          ...         ...  \n",
       "886           0           1  \n",
       "887           0           1  \n",
       "888           0           1  \n",
       "889           0           0  \n",
       "890           1           0  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.drop(['PassengerId','Name','Ticket','Cabin'], axis=1)\n",
    "new_df['Age'].fillna(int(df['Age'].mean()), inplace=True)\n",
    "new_df['Sex'] = new_df['Sex'].apply(lambda x : ['male', 'female'].index(x))\n",
    "new_df[['Embarked_C', 'Embarked_Q', 'Embarked_S']] = pd.get_dummies(new_df['Embarked'], dtype=int)\n",
    "new_df = new_df.drop(['Embarked'], axis=1)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(new_df.loc[:,new_df.columns!='Survived'], new_df['Survived'], test_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We get a brief summary of the data\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 2, 0, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "y = [0,1,0,0,0,0,1,0,5]\n",
    "np.bincount(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7601683029453016\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy:',metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvocationException",
     "evalue": "GraphViz's executables not found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvocationException\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[11], line 11\u001b[0m\n\u001b[0;32m      7\u001b[0m export_graphviz(clf, out_file\u001b[39m=\u001b[39mdot_data,\n\u001b[0;32m      8\u001b[0m                 filled\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, rounded\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m      9\u001b[0m                 special_characters\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,feature_names \u001b[39m=\u001b[39m X_train\u001b[39m.\u001b[39mcolumns)\n\u001b[0;32m     10\u001b[0m graph \u001b[39m=\u001b[39m pydotplus\u001b[39m.\u001b[39mgraph_from_dot_data(dot_data\u001b[39m.\u001b[39mgetvalue())\n\u001b[1;32m---> 11\u001b[0m graph\u001b[39m.\u001b[39;49mwrite_png(\u001b[39m'\u001b[39;49m\u001b[39mTitacnic_decision_tree.png\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m     12\u001b[0m Image(graph\u001b[39m.\u001b[39mcreate_png())\n",
      "File \u001b[1;32mc:\\Users\\Gael\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pydotplus\\graphviz.py:1810\u001b[0m, in \u001b[0;36mDot.__init__.<locals>.<lambda>\u001b[1;34m(path, f, prog)\u001b[0m\n\u001b[0;32m   1800\u001b[0m     f\u001b[39m.\u001b[39m\u001b[39m__doc__\u001b[39m \u001b[39m=\u001b[39m (\n\u001b[0;32m   1801\u001b[0m \u001b[39m        \u001b[39m\u001b[39m'''Refer to the docstring accompanying the'''\u001b[39;00m\n\u001b[0;32m   1802\u001b[0m \u001b[39m        \u001b[39m\u001b[39m''''create' method for more information.'''\u001b[39;00m\n\u001b[0;32m   1803\u001b[0m     )\n\u001b[0;32m   1805\u001b[0m \u001b[39mfor\u001b[39;00m frmt \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mformats \u001b[39m+\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mraw\u001b[39m\u001b[39m'\u001b[39m]:\n\u001b[0;32m   1806\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__setattr__\u001b[39m(\n\u001b[0;32m   1807\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mwrite_\u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m frmt,\n\u001b[0;32m   1808\u001b[0m         \u001b[39mlambda\u001b[39;00m path,\n\u001b[0;32m   1809\u001b[0m         f\u001b[39m=\u001b[39mfrmt,\n\u001b[1;32m-> 1810\u001b[0m         prog\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprog: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwrite(path, \u001b[39mformat\u001b[39;49m\u001b[39m=\u001b[39;49mf, prog\u001b[39m=\u001b[39;49mprog)\n\u001b[0;32m   1811\u001b[0m     )\n\u001b[0;32m   1813\u001b[0m     f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__dict__\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mwrite_\u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m frmt]\n\u001b[0;32m   1814\u001b[0m     f\u001b[39m.\u001b[39m\u001b[39m__doc__\u001b[39m \u001b[39m=\u001b[39m (\n\u001b[0;32m   1815\u001b[0m \u001b[39m        \u001b[39m\u001b[39m'''Refer to the docstring accompanying the'''\u001b[39;00m\n\u001b[0;32m   1816\u001b[0m \u001b[39m        \u001b[39m\u001b[39m''''write' method for more information.'''\u001b[39;00m\n\u001b[0;32m   1817\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\Gael\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pydotplus\\graphviz.py:1918\u001b[0m, in \u001b[0;36mDot.write\u001b[1;34m(self, path, prog, format)\u001b[0m\n\u001b[0;32m   1915\u001b[0m         fobj\u001b[39m.\u001b[39mwrite(data)\n\u001b[0;32m   1917\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1918\u001b[0m         fobj\u001b[39m.\u001b[39mwrite(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcreate(prog, \u001b[39mformat\u001b[39;49m))\n\u001b[0;32m   1919\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m   1920\u001b[0m     \u001b[39mif\u001b[39;00m close:\n",
      "File \u001b[1;32mc:\\Users\\Gael\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pydotplus\\graphviz.py:1959\u001b[0m, in \u001b[0;36mDot.create\u001b[1;34m(self, prog, format)\u001b[0m\n\u001b[0;32m   1957\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprogs \u001b[39m=\u001b[39m find_graphviz()\n\u001b[0;32m   1958\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprogs \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m-> 1959\u001b[0m         \u001b[39mraise\u001b[39;00m InvocationException(\n\u001b[0;32m   1960\u001b[0m             \u001b[39m'\u001b[39m\u001b[39mGraphViz\u001b[39m\u001b[39m\\'\u001b[39;00m\u001b[39ms executables not found\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m   1962\u001b[0m \u001b[39mif\u001b[39;00m prog \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprogs:\n\u001b[0;32m   1963\u001b[0m     \u001b[39mraise\u001b[39;00m InvocationException(\n\u001b[0;32m   1964\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mGraphViz\u001b[39m\u001b[39m\\'\u001b[39;00m\u001b[39ms executable \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m not found\u001b[39m\u001b[39m'\u001b[39m \u001b[39m%\u001b[39m prog)\n",
      "\u001b[1;31mInvocationException\u001b[0m: GraphViz's executables not found"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "from six import StringIO\n",
    "from IPython.display import Image\n",
    "import pydotplus\n",
    "\n",
    "dot_data = StringIO()\n",
    "export_graphviz(clf, out_file=dot_data,\n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True,feature_names = X_train.columns)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "graph.write_png('Titacnic_decision_tree.png')\n",
    "Image(graph.create_png())"
   ]
  }
 ],
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