titanic_survie.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data = pd.read_csv('./datasets/titanic_survive.csv')\n",
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the unnecessary columns\n",
    "titanic_data.drop(['Cabin'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the missing values in Age column with mean value\n",
    "titanic_data['Age'].fillna(titanic_data['Age'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the mode value of Embarked column\n",
    "titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "sns.countplot(x='Survived', data=titanic_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of survivors gender sex\n",
    "sns.countplot(x='Sex', data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.countplot(x='Sex', hue=\"Survived\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert categorical value to numerical value\n",
    "titanic_data.replace({'Sex': {'male': 1, 'female': 0}, 'Embarked': {'S': 0, 'C': 1, 'Q': 2}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>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>1</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>0</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>0</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>1</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>0</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>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>0</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>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>1</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>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",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare  Embarked  \n",
       "0         A/5 21171   7.2500         0  \n",
       "1          PC 17599  71.2833         1  \n",
       "2  STON/O2. 3101282   7.9250         0  \n",
       "3            113803  53.1000         0  \n",
       "4            373450   8.0500         0  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = titanic_data.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)\n",
    "Y = titanic_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniconda/base/envs/ai/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data :  0.8117977528089888\n"
     ]
    }
   ],
   "source": [
    "# accuracy score on training data\n",
    "X_train_prediction = model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
    "print('Accuracy on training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data :  0.7821229050279329\n"
     ]
    }
   ],
   "source": [
    "# accuracy score on test data\n",
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
    "print('Accuracy on test data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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