SVM.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine on Titanic Dataset\n",
    "#\n",
    "#### We try to predict with svm 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",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "    <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",
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       "      <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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       "      <th>4</th>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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      ],
      "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"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1- Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>53.1000</td>\n",
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       "      <th>886</th>\n",
       "      <td>0</td>\n",
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       "      <td>27.0</td>\n",
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       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>887</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      ],
      "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": 3,
     "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": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = svm.SVC()"
   ]
  },
  {
   "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>SVC()</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.67      0.93      0.78       441\\n           1       0.69      0.26      0.38       272\\n\\n    accuracy                           0.67       713\\n   macro avg       0.68      0.59      0.58       713\\nweighted avg       0.68      0.67      0.62       713\\n'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6732117812061711"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_pred, labels=[0, 1])\n",
    "\n",
    "plt.figure(figsize=(4,3), dpi=100)\n",
    "sns.set(font_scale=1)\n",
    "\n",
    "ax = sns.heatmap(conf_matrix, annot=True, fmt='d')\n",
    "ax.set_xlabel(\"Predicted\", fontsize=14, labelpad=20)\n",
    "ax.xaxis.set_ticklabels(['Died', 'Survived'])\n",
    "\n",
    "ax.set_ylabel(\"Truth\", fontsize=14, labelpad=20)\n",
    "ax.yaxis.set_ticklabels(['Died', 'Survived'])\n",
    "\n",
    "ax.set_title(\"Confusion Matrix for Survivor prediction on Titanic\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}