Classifying Passengers Who Sailed on the Titanic.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
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     "data": {
      "text/html": [
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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",
       "  </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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('data/titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "# see if there is any missing values \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>22.0</td>\n",
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       "      <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>1</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>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Survived   Age  Sex_female  Sex_male  Pclass_1  Pclass_2  Pclass_3\n",
       "0         0  22.0           0         1         0         0         1\n",
       "1         1  38.0           1         0         1         0         0\n",
       "2         1  26.0           1         0         0         0         1\n",
       "3         1  35.0           1         0         1         0         0\n",
       "4         0  35.0           0         1         0         0         1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[[\n",
    "    'Survived', 'Age', 'Sex', 'Pclass'\n",
    "]]\n",
    "df = pd.get_dummies(df, columns=['Sex', 'Pclass'])\n",
    "df.dropna(inplace=True) # Drop null-values\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the dataset for training and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "x = df.drop('Survived', axis=1)\n",
    "y = df['Survived']\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    x, y, test_size=0.2, stratify=y, random_state=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8321678321678322"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(random_state=0)\n",
    "model.fit(x_train, y_train)\n",
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7857480547621394"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(model, x, y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x209d0d8f040>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display confusion matrix showing how model performed during testing\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import ConfusionMatrixDisplay as cmd\n",
    "cmd.from_estimator(\n",
    "    model, x_test, y_test, display_labels=['Perished', 'Survived'],\n",
    "    cmap='Blues', xticks_rotation='vertical'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8541666666666666"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7068965517241379"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall = recall_score(y_test, y_pred)\n",
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9176470588235294"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensitivity = recall\n",
    "specificity = recall_score(y_test, y_pred, pos_label=0)\n",
    "specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Age</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Pclass_1</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>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex_female  Sex_male  Pclass_1  Pclass_2  Pclass_3\n",
       "0   30           1         0         1         0         0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female = pd.DataFrame({\n",
    "    'Age': 30,\n",
    "    'Sex_female': 1,\n",
    "    'Sex_male': 0,\n",
    "    'Pclass_1': 1,\n",
    "    'Pclass_2': 0,\n",
    "    'Pclass_3': 0\n",
    "}, index=[0])\n",
    "female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9158081604893185"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability = model.predict_proba(female)[0][1]\n",
    "probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Age</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Pclass_1</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>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex_female  Sex_male  Pclass_1  Pclass_2  Pclass_3\n",
       "0   60           0         1         0         0         1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male = pd.DataFrame({\n",
    "    'Age': 60,\n",
    "    'Sex_female': 0,\n",
    "    'Sex_male': 1,\n",
    "    'Pclass_1': 0,\n",
    "    'Pclass_2': 0,\n",
    "    'Pclass_3': 1\n",
    "}, index=[0])\n",
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.029480447463569155"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability = model.predict_proba(male)[0][1]\n",
    "probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Age</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Pclass_1</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>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex_female  Sex_male  Pclass_1  Pclass_2  Pclass_3\n",
       "0   12           0         1         0         1         0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male = pd.DataFrame({\n",
    "    'Age': 12,\n",
    "    'Sex_female': 0,\n",
    "    'Sex_male': 1,\n",
    "    'Pclass_1': 0,\n",
    "    'Pclass_2': 1,\n",
    "    'Pclass_3': 0\n",
    "}, index=[0])\n",
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40122804790722405"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability = model.predict_proba(male)[0][1]\n",
    "probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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